Download Free Download : Pluralsight - Building Deep Learning Solutions with PyTorch
mp4 | Video: h264,1280X720 | Audio: AAC, 44.1 KHz
Genre:eLearning | Language: English | Size:2.51 GB
Files Included :
1 Course Overview.mp4 (3.7 MB)
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01 Version Check.mp4 (550.56 KB)
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02 Module Overview.mp4 (1.3 MB)
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03 Prerequisites and Course Outline.mp4 (2.8 MB)
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04 Representation Learning Using Neural Networks.mp4 (10.35 MB)
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05 Neuron as a Mathematical Function.mp4 (9.26 MB)
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06 Activation Functions.mp4 (7.32 MB)
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07 Introducing PyTorch.mp4 (5.73 MB)
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08 TensorFlow and PyTorch.mp4 (6.75 MB)
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09 Demo - PyTorch Install and Setup.mp4 (9.51 MB)
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10 Summary.mp4 (1.4 MB)
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01 Module Overview.mp4 (3.21 MB)
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02 Demo - Creating and Initializing Tensors.mp4 (13.19 MB)
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03 Demo - Simple Operations on Tensors.mp4 (10.82 MB)
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04 Demo - Elementwise and Matrix Operations on Tensors.mp4 (7.69 MB)
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05 Demo - Converting between PyTorch Tensors and NumPy Arrays.mp4 (8.39 MB)
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06 PyTorch Support for CUDA Devices.mp4 (9.47 MB)
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07 Demo - Setting up a Deep Learning VM to Work with GPUs.mp4 (15.47 MB)
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08 Demo - Creating Tensors on CUDA-enabled Devices.mp4 (6.93 MB)
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09 Demo - Working with the Device Context Manager.mp4 (9.23 MB)
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10 Summary.mp4 (1.54 MB)
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01 Module Overview.mp4 (1.54 MB)
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02 Gradient Descent Optimization.mp4 (6.32 MB)
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03 Forward and Backward Passes.mp4 (4.92 MB)
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04 Calculating Gradients.mp4 (7.31 MB)
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05 Using Gradients to Update Model Parameters.mp4 (5.9 MB)
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06 Two Passes in Reverse Mode Automatic Differentiation.mp4 (5.99 MB)
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07 Demo - Introducing Autograd.mp4 (10.08 MB)
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08 Demo - Working with Gradients.mp4 (7.61 MB)
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09 Demo - Variables and Tensors.mp4 (3.64 MB)
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10 Demo - Training a Linear Model Using Autograd.mp4 (15.4 MB)
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11 Summary.mp4 (2.07 MB)
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01 Module Overview.mp4 (883.38 KB)
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02 Static vs Dynamic Computation Graphs.mp4 (11.53 MB)
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03 Dynamic Computation Graphs in PyTorch.mp4 (1.83 MB)
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04 Demo - Installing Tensorflow, Graphviz, and Hidden Layer.mp4 (3.04 MB)
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05 Demo - Building Dynamic Computations Graphs with PyTorch.mp4 (4.14 MB)
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06 Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer.mp4 (5.35 MB)
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07 Demo - Building Static Computation Graphs with Tensorflow.mp4 (11.05 MB)
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08 Demo - Visualizing Tensorflow Graphs with Tensorboard.mp4 (3.92 MB)
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09 Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution.mp4 (5.98 MB)
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10 Debugging in PyTorch and Tensorflow.mp4 (2.13 MB)
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11 Summary and Further Study.mp4 (2.27 MB)
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1 Course Overview.mp4 (3.81 MB)
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01 Version Check.mp4 (560.53 KB)
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02 Module Overview.mp4 (1.63 MB)
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03 Prerequisites and Course Outline.mp4 (1.99 MB)
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04 CUDA Support in PyTorch.mp4 (10.08 MB)
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05 Exploring PyTorch Install Options on a Local Machine.mp4 (4.38 MB)
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06 Setting up a Virtual Machine.mp4 (9.41 MB)
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07 Installing PyTorch with CPU Support Using Conda.mp4 (19.17 MB)
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08 Installing PyTorch with CPU Support Using Pip.mp4 (10.37 MB)
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09 Adding GPU Support to the VM and Installing the CUDA Toolkit.mp4 (15.1 MB)
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10 Installing PyTorch with GPU Support Using Conda.mp4 (9.74 MB)
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11 Installing PyTorch with CUDA Support Using Pip.mp4 (5.37 MB)
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12 Module Summary.mp4 (1.87 MB)
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1 Module Overview.mp4 (1.77 MB)
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2 Linear Regression.mp4 (6.28 MB)
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3 Finding the Best Fit Line.mp4 (5.23 MB)
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4 Gradient Descent.mp4 (7.27 MB)
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5 Training a Simple Neural Network with One Neuron.mp4 (12.18 MB)
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7 Preventing Overfitting Using Regularization.mp4 (7.32 MB)
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9 Module Summary.mp4 (2.3 MB)
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01 Module Overview.mp4 (1.73 MB)
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02 Training a Neural Network Forward and Backward Passes.mp4 (4.16 MB)
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03 Optimizers.mp4 (5.76 MB)
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04 Building a Neural Network Using PyTorch Layers.mp4 (10.46 MB)
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05 Training a Neural Network Using Optimizers.mp4 (4.77 MB)
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06 Dropout.mp4 (5.66 MB)
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07 Epochs and Batches.mp4 (2.59 MB)
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08 Exploring the Bike Sharing Dataset.mp4 (11.75 MB)
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09 Using Datasets and Data Loaders in PyTorch.mp4 (5.38 MB)
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10 Building and Train a Neural Network for Bike Sharing Demand Prediction.mp4 (11.88 MB)
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11 Working with Different Neural Network Architectures.mp4 (9.11 MB)
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12 Module Summary.mp4 (1.95 MB)
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01 Module Overview.mp4 (1.77 MB)
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02 Softmax and Cross Entropy.mp4 (6.67 MB)
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03 Softmax and LogSoftmax.mp4 (4.48 MB)
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04 Evaluating Classifiers.mp4 (3.28 MB)
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05 Exploring the Graduate Admissions Dataset.mp4 (9.86 MB)
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06 Preprocessing the Data.mp4 (8.19 MB)
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07 Building a Custom Neural Network.mp4 (10.65 MB)
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08 Training and Evaluating the Neural Network.mp4 (8 MB)
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09 Customizing and Evaluating Different Models.mp4 (10.53 MB)
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10 Summary and Further Study.mp4 (2.32 MB)
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1 Course Overview.mp4 (2.92 MB)
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01 Version Check.mp4 (577.35 KB)
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02 Module Overview.mp4 (1.28 MB)
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03 Prerequisites and Course Outline.mp4 (1.82 MB)
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04 Machine Learning on the Cloud.mp4 (3.83 MB)
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05 PyTorch - Taxonomy of Solutions.mp4 (4.76 MB)
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06 Introducing SageMaker.mp4 (3.83 MB)
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07 Creating a SageMaker Notebook Instance.mp4 (17.35 MB)
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08 Prototyping a PyTorch Model on SageMaker Notebooks.mp4 (18.17 MB)
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09 PyTorch Estimators on SageMaker.mp4 (2.34 MB)
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10 Distributed Data Loading in PyTorch.mp4 (13.62 MB)
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11 Distributed Training in PyTorch.mp4 (17.78 MB)
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12 Using PyTorch Estimators for Distributed Training.mp4 (16.23 MB)
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13 Model Deployment and Prediction Using Estimators.mp4 (10.28 MB)
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14 AWS Deep Learning AMIs.mp4 (2.61 MB)
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15 Instantiating a Deep Learning VM.mp4 (18.57 MB)
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16 Building Models with GPU Support on the AWS Deep Learning VM.mp4 (13.2 MB)
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01 Module Overview.mp4 (1.42 MB)
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02 Introducing Azure Machine Learning Service.mp4 (2.64 MB)
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03 Prototyping PyTorch Models on Azure Notebooks.mp4 (16.01 MB)
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04 Azure Machine Learning Service Workflow.mp4 (4.5 MB)
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05 Understanding Terms in Azure Machine Learning.mp4 (3.81 MB)
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06 Horovod for Distributed Training.mp4 (1.84 MB)
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07 Distributed Training in PyTorch Using the Horovod Framework.mp4 (22.73 MB)
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08 Instantiating the PyTorch Estimator for Distributed Training.mp4 (17.23 MB)
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09 Distributed Run Using the PyTorch Estimator.mp4 (11.05 MB)
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10 The Azure Deep Learning VM.mp4 (2.52 MB)
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11 Instantiating an Azure Deep Learning VM.mp4 (15.53 MB)
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12 Building PyTorch Models with GPU Support on Azure Deep Learning VMs.mp4 (12.95 MB)
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1 Module Overview.mp4 (1004.95 KB)
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2 Cloud Datalab and Deep Learning VMs.mp4 (4.11 MB)
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3 Setting up a Cloud Datalab VM.mp4 (16.9 MB)
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4 Prototyping PyTorch Models Using Cloud Datalab.mp4 (5.43 MB)
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6 Using JupyterLab on a GCP Deep Learning VM.mp4 (4.64 MB)
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7 Summary and Further Study.mp4 (2.04 MB)
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1 Course Overview.mp4 (3.44 MB)
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01 Version Check.mp4 (564.49 KB)
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02 Module Overview.mp4 (1.71 MB)
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03 Prerequisites and Course Outline.mp4 (2.3 MB)
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04 Single Channel and Multichannel Images.mp4 (6.47 MB)
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05 Preprocessing Images to Train Robust Models.mp4 (8.46 MB)
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06 Setting up a Deep Learning VM.mp4 (12.06 MB)
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07 Image Preprocessing - Resizing and Rescaling Images.mp4 (12.74 MB)
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08 Cropping and Denoising Images.mp4 (11.21 MB)
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09 Standardizing Images in PyTorch.mp4 (8.89 MB)
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10 ZCA Whitening to Decorrelate Features.mp4 (5.5 MB)
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11 Image Transformations Using PyTorch Libraries.mp4 (5.72 MB)
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12 Normalizing Images Using Mean and Standard Deviation.mp4 (10.31 MB)
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13 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (2.25 MB)
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2 Deep Neural Networks to Work with Images.mp4 (10.8 MB)
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3 Loading and Processing MNIST Images.mp4 (11.78 MB)
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6 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (1.76 MB)
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2 Local Receptive Fields.mp4 (4.28 MB)
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3 Understanding Convolution.mp4 (5.98 MB)
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4 Convolutional Layers.mp4 (11.06 MB)
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5 Pooling Layers.mp4 (6.38 MB)
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6 Typical CNN Architecture.mp4 (5.89 MB)
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7 Applying Convolutional and Pooling Layers.mp4 (17.28 MB)
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8 Module Summary.mp4 (1.77 MB)
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01 Module Overview.mp4 (2 MB)
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02 Zero Padding and Stride Size.mp4 (6.49 MB)
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03 Batch Normalization.mp4 (7.18 MB)
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04 Activation Functions.mp4 (3.72 MB)
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05 Feature Map Size Calculations.mp4 (3.18 MB)
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06 Preparing and Exploring Image Data.mp4 (7.83 MB)
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07 Setting up a Convolutional Neural Network.mp4 (11.25 MB)
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08 Training a CNN.mp4 (10.25 MB)
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09 Hyperparameter Tuning.mp4 (10.53 MB)
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10 Module Summary.mp4 (2.1 MB)
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1 Module Overview.mp4 (1.59 MB)
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2 Preparing the CIFAR-10 Dataset.mp4 (7.73 MB)
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3 Setting up the CNN.mp4 (6.92 MB)
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4 Training the CNN.mp4 (8.45 MB)
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5 Choosing Different Activation Functions.mp4 (7.7 MB)
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6 Choosing Pooling Layers.mp4 (7.2 MB)
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7 Choosing Convolution Kernel Sizes.mp4 (8.24 MB)
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8 Additional Convolution Layers and Different Kernel Size.mp4 (8.5 MB)
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9 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (1.81 MB)
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2 Transfer Learning.mp4 (7.72 MB)
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3 Using the Resnet-18 Pretrained Model.mp4 (12.04 MB)
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4 The Train Function to Find the Best Model Weights.mp4 (8.45 MB)
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5 Predictions Using Pretrained Models.mp4 (4.68 MB)
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6 Cleaning up Resources.mp4 (2.25 MB)
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7 Summary and Further Study.mp4 (2.13 MB)
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1 Course Overview.mp4 (3.85 MB)
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01 Version Check.mp4 (554.89 KB)
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02 Module Overview.mp4 (1.83 MB)
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03 Prerequisites and Course Outline.mp4 (2.3 MB)
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04 Content, Style, and Target Images.mp4 (7.26 MB)
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05 Training the Target Image for Style Transfer.mp4 (12.48 MB)
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06 Content Loss.mp4 (6.22 MB)
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07 Style Loss - Cosine Similarity and Dot Products.mp4 (5.37 MB)
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08 Style Loss - Gram Matrix.mp4 (5.75 MB)
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09 Setting up a Deep Learning Virtual Machine.mp4 (11.45 MB)
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10 Using Convolution Filters to Detect Features.mp4 (14.67 MB)
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11 Module Summary.mp4 (1.85 MB)
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1 Module Overview.mp4 (1.82 MB)
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2 Pretrained Models for Style Transfer.mp4 (3.82 MB)
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3 Loading the VGG19 Pretrained Model.mp4 (7.11 MB)
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4 Exploring and Transforming the Content and Style Images.mp4 (14.27 MB)
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5 Extracting Feature Maps from the Content and Style Images.mp4 (7.96 MB)
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6 Calculating the Gram Matrix to Extract Style Information.mp4 (5.89 MB)
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7 Training the Target Image to Perform Style Transfer.mp4 (12.44 MB)
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8 Style Transfer Using AlexNet.mp4 (14.74 MB)
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9 Module Summary.mp4 (1.01 MB)
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01 Module Overview.mp4 (2.06 MB)
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02 Understanding Generative Adversarial Networks (GANs).mp4 (9.46 MB)
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03 Training a GAN.mp4 (4.96 MB)
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04 Understanding the Leaky ReLU Activation Function.mp4 (8.79 MB)
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05 Loading and Exploring the MNIST Handwritten Digit Images.mp4 (9.01 MB)
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06 Setting up the Generator and Discriminator Neural Networks.mp4 (8.3 MB)
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07 Training the Discriminator.mp4 (9.39 MB)
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08 Training the Generator and Generating Fake Images.mp4 (7.97 MB)
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09 Cleaning up Resources.mp4 (2.66 MB)
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10 Summary and Further Study.mp4 (2.59 MB)
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1 Course Overview.mp4 (3.35 MB)
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1 Version Check.mp4 (560.2 KB)
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2 Module Overview.mp4 (1.99 MB)
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3 Prerequisites and Course Outline.mp4 (2.26 MB)
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4 RNNs for Natural Language Processing.mp4 (5.57 MB)
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5 Recurrent Neurons.mp4 (6.8 MB)
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6 Back Propagation through Time.mp4 (7.46 MB)
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7 Coping with Vanishing and Exploding Gradients.mp4 (9.61 MB)
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8 Long Memory Cells.mp4 (10.05 MB)
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9 Module Summary.mp4 (2.23 MB)
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01 Module Overview.mp4 (1.81 MB)
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02 Word Embeddings to Represent Text Data.mp4 (7.27 MB)
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03 Introducing torchtext to Process Text Data.mp4 (3.62 MB)
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04 Feeding Text Data into RNNs.mp4 (5.15 MB)
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05 Setup and Data Cleaning.mp4 (8.15 MB)
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06 Using Torchtext to Process Text Data.mp4 (18.78 MB)
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07 Designing an RNN for Binary Text Classification.mp4 (11.08 MB)
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08 Training the RNN.mp4 (10.96 MB)
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09 Using LSTM Cells and Dropout.mp4 (5.59 MB)
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10 Module Summary.mp4 (1.91 MB)
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1 Module Overview.mp4 (2.05 MB)
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2 Language Prediction Based on Names.mp4 (3.22 MB)
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3 Loading and Cleaning Data.mp4 (14.07 MB)
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4 Helper Functions to One Hot Encode Names.mp4 (6.32 MB)
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5 Designing an RNN for Multiclass Text Classification.mp4 (16.32 MB)
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6 Predicting Language from Names.mp4 (13.01 MB)
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7 Module Summary.mp4 (1.87 MB)
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01 Module Overview.mp4 (2.35 MB)
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02 Numeric Representations of Words.mp4 (4.39 MB)
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03 Word Embeddings Capture Context and Meaning.mp4 (6.44 MB)
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04 Generating Analogies Using GloVe Embeddings.mp4 (16.52 MB)
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05 Multilayer RNNs.mp4 (2.71 MB)
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06 Bidirectional RNNs.mp4 (6.71 MB)
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07 Data Cleaning and Preparation.mp4 (17.63 MB)
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08 Designing a Multilayer Bidirectional RNN.mp4 (11.32 MB)
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09 Performing Sentiment Analysis Using an RNN.mp4 (7.81 MB)
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10 Module Summary.mp4 (1.99 MB)
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01 Module Overview.mp4 (1.96 MB)
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02 Using Sequences and Vectors with RNNs.mp4 (5.44 MB)
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04 Representing Input and Target Sentences.mp4 (2.7 MB)
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05 Teacher Forcing.mp4 (4.96 MB)
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07 Preparing Sentence Pairs.mp4 (10.68 MB)
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08 Designing the Encoder and Decoder.mp4 (10.19 MB)
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10 Translating Sentences.mp4 (9.27 MB)
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11 Summary and Further Study.mp4 (3.16 MB)
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1 Course Overview.mp4 (3.43 MB)
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01 Version Check.mp4 (612.26 KB)
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02 Module Overview.mp4 (2.18 MB)
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03 Prerequisites and Course Outline.mp4 (1.99 MB)
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04 Introducing Transfer Learning.mp4 (7.2 MB)
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06 Categorizing Transfer Learning.mp4 (9.73 MB)
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07 Transfer Learning Scenarios.mp4 (7.92 MB)
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08 Freeze or Fine-tune Layers.mp4 (7.45 MB)
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09 Benefits of Transfer Learning.mp4 (3.66 MB)
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10 Pre-trained Models in PyTorch.mp4 (9.67 MB)
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12 Exploring Pre-trained Models in PyTorch.mp4 (21.1 MB)
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13 Module Summary.mp4 (1.9 MB)
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1 Module Overview.mp4 (2.66 MB)
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8 Fine-tuning Top Layers.mp4 (7.13 MB)
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9 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (2.34 MB)
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2 Exploring and Loading the Chest X-Ray Dataset.mp4 (10.25 MB)
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3 Training a Model from Scratch.mp4 (10.19 MB)
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4 Exploring and Loading the Natural Images Dataset.mp4 (7.65 MB)
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5 Fine-tuning the Network.mp4 (8.92 MB)
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6 Cleaning up Resources.mp4 (2.54 MB)
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7 Summary and Further Study.mp4 (1.94 MB)
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1 Course Overview.mp4 (4.13 MB)
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01 Version Check.mp4 (575.06 KB)
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02 Prerequisites and Course Outline.mp4 (2.68 MB)
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03 Structural and Predictive Models.mp4 (8 MB)
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04 Demo - Install and Setup Pytorch.mp4 (8.18 MB)
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05 Demo - Preparing Data.mp4 (12.32 MB)
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06 Demo - Building a Simple Neural Network to Perform Regression.mp4 (11.7 MB)
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07 Demo - Exploring the Diamonds Dataset.mp4 (9.54 MB)
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08 Demo - Preparing and Processing Data.mp4 (10.25 MB)
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09 Demo - Building and Training a Regression Model.mp4 (16.7 MB)
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10 Demo - Exploring and Preprocessing Data.mp4 (15.6 MB)
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11 Demo - Defining the Neural Network and Helper Functions.mp4 (12.79 MB)
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1 Text as Sequential Data.mp4 (4.33 MB)
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2 The Recurrent Neuron.mp4 (5.18 MB)
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3 RNN Training and Long Memory Cells.mp4 (8.18 MB)
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4 RNN to Generate Names in Languages.mp4 (4.94 MB)
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5 Demo - Loading and Preparing Training Data.mp4 (11.14 MB)
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6 Demo - Setting up Helper Functions.mp4 (9.87 MB)
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7 Demo - Defining the RNN.mp4 (18.26 MB)
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8 Demo - Training the RNN and Generating Names.mp4 (16.41 MB)
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01 Finding Patterns in Data.mp4 (4.53 MB)
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02 Association Rule Learning.mp4 (3.6 MB)
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03 Clustering.mp4 (4.84 MB)
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04 Content Based Approaches to Recommendations.mp4 (7.14 MB)
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05 Collaborative Filtering.mp4 (5.66 MB)
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06 Nearest Neighborhood.mp4 (4 MB)
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07 Matrix Factorization.mp4 (9.28 MB)
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08 Alternating Least Squares to Estimate the Ratings Matrix.mp4 (5.8 MB)
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09 Evaluation Metrics vs Loss Metrics.mp4 (4.08 MB)
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10 Mean Average Precision @ K.mp4 (11.27 MB)
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11 Demo - Initializing the Ratings Matrix.mp4 (11.56 MB)
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12 Demo - Setting up the Neural Network.mp4 (12.31 MB)
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13 Demo - The Train Helper Function.mp4 (20.3 MB)
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14 Demo - The Evaluate Helper Function.mp4 (6.51 MB)
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16 Summary and Further Study.mp4 (2.12 MB)
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1 Course Overview.mp4 (3.78 MB)
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01 Version Check.mp4 (606.15 KB)
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02 Module Overview.mp4 (2.89 MB)
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03 Prerequisites and Course Outline.mp4 (1.88 MB)
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04 Saving and Loading PyTorch Models.mp4 (10.23 MB)
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05 Building and Training a Classifier Model.mp4 (11.85 MB)
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06 Saving and Loading Models Using torch save().mp4 (16.55 MB)
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07 Saving Model Using the state dict.mp4 (15.29 MB)
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08 Saving and Loading Checkpoints.mp4 (10.08 MB)
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09 Introducing ONNX.mp4 (2.76 MB)
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10 Exporting a Model to ONNX and Loading in Caffe2.mp4 (18.34 MB)
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11 Module Summary.mp4 (1.88 MB)
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1 Module Overview.mp4 (1.67 MB)
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3 Training Using Multiple Processes.mp4 (14.27 MB)
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5 Training on Multiple GPUs.mp4 (12.41 MB)
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6 Module Summary.mp4 (1.69 MB)
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1 Module Overview.mp4 (1.97 MB)
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2 Distributed Training on the Cloud.mp4 (5.48 MB)
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3 Setting up a SageMaker Notebook Instance.mp4 (8.88 MB)
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4 Setting up Training and Test Data Loaders.mp4 (9.46 MB)
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5 Define the Training Function.mp4 (9.4 MB)
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8 Module Summary.mp4 (1.54 MB)
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1 Module Overview.mp4 (1.78 MB)
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2 Exploring Options to Deploy PyTorch Models.mp4 (6.18 MB)
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4 Creating a Flask App to Serve the PyTorch Model.mp4 (10.87 MB)
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5 Using the Model for Prediction.mp4 (4.51 MB)
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6 Installing Docker.mp4 (5.56 MB)
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7 Creating and Using a Clipper Cluster for Prediction.mp4 (17.09 MB)
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9 Summary and Further Study.mp4 (2.01 MB)
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01 Version Check.mp4 (550.56 KB)
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02 Module Overview.mp4 (1.3 MB)
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03 Prerequisites and Course Outline.mp4 (2.8 MB)
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04 Representation Learning Using Neural Networks.mp4 (10.35 MB)
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05 Neuron as a Mathematical Function.mp4 (9.26 MB)
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06 Activation Functions.mp4 (7.32 MB)
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07 Introducing PyTorch.mp4 (5.73 MB)
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08 TensorFlow and PyTorch.mp4 (6.75 MB)
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09 Demo - PyTorch Install and Setup.mp4 (9.51 MB)
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10 Summary.mp4 (1.4 MB)
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01 Module Overview.mp4 (3.21 MB)
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02 Demo - Creating and Initializing Tensors.mp4 (13.19 MB)
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03 Demo - Simple Operations on Tensors.mp4 (10.82 MB)
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04 Demo - Elementwise and Matrix Operations on Tensors.mp4 (7.69 MB)
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05 Demo - Converting between PyTorch Tensors and NumPy Arrays.mp4 (8.39 MB)
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06 PyTorch Support for CUDA Devices.mp4 (9.47 MB)
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07 Demo - Setting up a Deep Learning VM to Work with GPUs.mp4 (15.47 MB)
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08 Demo - Creating Tensors on CUDA-enabled Devices.mp4 (6.93 MB)
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09 Demo - Working with the Device Context Manager.mp4 (9.23 MB)
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10 Summary.mp4 (1.54 MB)
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01 Module Overview.mp4 (1.54 MB)
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02 Gradient Descent Optimization.mp4 (6.32 MB)
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03 Forward and Backward Passes.mp4 (4.92 MB)
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04 Calculating Gradients.mp4 (7.31 MB)
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05 Using Gradients to Update Model Parameters.mp4 (5.9 MB)
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06 Two Passes in Reverse Mode Automatic Differentiation.mp4 (5.99 MB)
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07 Demo - Introducing Autograd.mp4 (10.08 MB)
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08 Demo - Working with Gradients.mp4 (7.61 MB)
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09 Demo - Variables and Tensors.mp4 (3.64 MB)
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10 Demo - Training a Linear Model Using Autograd.mp4 (15.4 MB)
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11 Summary.mp4 (2.07 MB)
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01 Module Overview.mp4 (883.38 KB)
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02 Static vs Dynamic Computation Graphs.mp4 (11.53 MB)
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03 Dynamic Computation Graphs in PyTorch.mp4 (1.83 MB)
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04 Demo - Installing Tensorflow, Graphviz, and Hidden Layer.mp4 (3.04 MB)
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05 Demo - Building Dynamic Computations Graphs with PyTorch.mp4 (4.14 MB)
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06 Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer.mp4 (5.35 MB)
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07 Demo - Building Static Computation Graphs with Tensorflow.mp4 (11.05 MB)
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08 Demo - Visualizing Tensorflow Graphs with Tensorboard.mp4 (3.92 MB)
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09 Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution.mp4 (5.98 MB)
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10 Debugging in PyTorch and Tensorflow.mp4 (2.13 MB)
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11 Summary and Further Study.mp4 (2.27 MB)
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1 Course Overview.mp4 (3.81 MB)
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01 Version Check.mp4 (560.53 KB)
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02 Module Overview.mp4 (1.63 MB)
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03 Prerequisites and Course Outline.mp4 (1.99 MB)
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04 CUDA Support in PyTorch.mp4 (10.08 MB)
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05 Exploring PyTorch Install Options on a Local Machine.mp4 (4.38 MB)
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06 Setting up a Virtual Machine.mp4 (9.41 MB)
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07 Installing PyTorch with CPU Support Using Conda.mp4 (19.17 MB)
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08 Installing PyTorch with CPU Support Using Pip.mp4 (10.37 MB)
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09 Adding GPU Support to the VM and Installing the CUDA Toolkit.mp4 (15.1 MB)
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10 Installing PyTorch with GPU Support Using Conda.mp4 (9.74 MB)
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11 Installing PyTorch with CUDA Support Using Pip.mp4 (5.37 MB)
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12 Module Summary.mp4 (1.87 MB)
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1 Module Overview.mp4 (1.77 MB)
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2 Linear Regression.mp4 (6.28 MB)
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3 Finding the Best Fit Line.mp4 (5.23 MB)
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4 Gradient Descent.mp4 (7.27 MB)
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5 Training a Simple Neural Network with One Neuron.mp4 (12.18 MB)
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7 Preventing Overfitting Using Regularization.mp4 (7.32 MB)
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9 Module Summary.mp4 (2.3 MB)
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01 Module Overview.mp4 (1.73 MB)
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02 Training a Neural Network Forward and Backward Passes.mp4 (4.16 MB)
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03 Optimizers.mp4 (5.76 MB)
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04 Building a Neural Network Using PyTorch Layers.mp4 (10.46 MB)
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05 Training a Neural Network Using Optimizers.mp4 (4.77 MB)
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06 Dropout.mp4 (5.66 MB)
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07 Epochs and Batches.mp4 (2.59 MB)
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08 Exploring the Bike Sharing Dataset.mp4 (11.75 MB)
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09 Using Datasets and Data Loaders in PyTorch.mp4 (5.38 MB)
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10 Building and Train a Neural Network for Bike Sharing Demand Prediction.mp4 (11.88 MB)
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11 Working with Different Neural Network Architectures.mp4 (9.11 MB)
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12 Module Summary.mp4 (1.95 MB)
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01 Module Overview.mp4 (1.77 MB)
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02 Softmax and Cross Entropy.mp4 (6.67 MB)
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03 Softmax and LogSoftmax.mp4 (4.48 MB)
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04 Evaluating Classifiers.mp4 (3.28 MB)
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05 Exploring the Graduate Admissions Dataset.mp4 (9.86 MB)
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06 Preprocessing the Data.mp4 (8.19 MB)
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07 Building a Custom Neural Network.mp4 (10.65 MB)
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08 Training and Evaluating the Neural Network.mp4 (8 MB)
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09 Customizing and Evaluating Different Models.mp4 (10.53 MB)
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10 Summary and Further Study.mp4 (2.32 MB)
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1 Course Overview.mp4 (2.92 MB)
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01 Version Check.mp4 (577.35 KB)
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02 Module Overview.mp4 (1.28 MB)
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03 Prerequisites and Course Outline.mp4 (1.82 MB)
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04 Machine Learning on the Cloud.mp4 (3.83 MB)
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05 PyTorch - Taxonomy of Solutions.mp4 (4.76 MB)
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06 Introducing SageMaker.mp4 (3.83 MB)
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07 Creating a SageMaker Notebook Instance.mp4 (17.35 MB)
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08 Prototyping a PyTorch Model on SageMaker Notebooks.mp4 (18.17 MB)
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09 PyTorch Estimators on SageMaker.mp4 (2.34 MB)
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10 Distributed Data Loading in PyTorch.mp4 (13.62 MB)
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11 Distributed Training in PyTorch.mp4 (17.78 MB)
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12 Using PyTorch Estimators for Distributed Training.mp4 (16.23 MB)
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13 Model Deployment and Prediction Using Estimators.mp4 (10.28 MB)
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14 AWS Deep Learning AMIs.mp4 (2.61 MB)
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15 Instantiating a Deep Learning VM.mp4 (18.57 MB)
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16 Building Models with GPU Support on the AWS Deep Learning VM.mp4 (13.2 MB)
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01 Module Overview.mp4 (1.42 MB)
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02 Introducing Azure Machine Learning Service.mp4 (2.64 MB)
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03 Prototyping PyTorch Models on Azure Notebooks.mp4 (16.01 MB)
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04 Azure Machine Learning Service Workflow.mp4 (4.5 MB)
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05 Understanding Terms in Azure Machine Learning.mp4 (3.81 MB)
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06 Horovod for Distributed Training.mp4 (1.84 MB)
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07 Distributed Training in PyTorch Using the Horovod Framework.mp4 (22.73 MB)
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08 Instantiating the PyTorch Estimator for Distributed Training.mp4 (17.23 MB)
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09 Distributed Run Using the PyTorch Estimator.mp4 (11.05 MB)
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10 The Azure Deep Learning VM.mp4 (2.52 MB)
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11 Instantiating an Azure Deep Learning VM.mp4 (15.53 MB)
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12 Building PyTorch Models with GPU Support on Azure Deep Learning VMs.mp4 (12.95 MB)
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1 Module Overview.mp4 (1004.95 KB)
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2 Cloud Datalab and Deep Learning VMs.mp4 (4.11 MB)
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3 Setting up a Cloud Datalab VM.mp4 (16.9 MB)
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4 Prototyping PyTorch Models Using Cloud Datalab.mp4 (5.43 MB)
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6 Using JupyterLab on a GCP Deep Learning VM.mp4 (4.64 MB)
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7 Summary and Further Study.mp4 (2.04 MB)
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1 Course Overview.mp4 (3.44 MB)
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01 Version Check.mp4 (564.49 KB)
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02 Module Overview.mp4 (1.71 MB)
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03 Prerequisites and Course Outline.mp4 (2.3 MB)
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04 Single Channel and Multichannel Images.mp4 (6.47 MB)
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05 Preprocessing Images to Train Robust Models.mp4 (8.46 MB)
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06 Setting up a Deep Learning VM.mp4 (12.06 MB)
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07 Image Preprocessing - Resizing and Rescaling Images.mp4 (12.74 MB)
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08 Cropping and Denoising Images.mp4 (11.21 MB)
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09 Standardizing Images in PyTorch.mp4 (8.89 MB)
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10 ZCA Whitening to Decorrelate Features.mp4 (5.5 MB)
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11 Image Transformations Using PyTorch Libraries.mp4 (5.72 MB)
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12 Normalizing Images Using Mean and Standard Deviation.mp4 (10.31 MB)
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13 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (2.25 MB)
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2 Deep Neural Networks to Work with Images.mp4 (10.8 MB)
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3 Loading and Processing MNIST Images.mp4 (11.78 MB)
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6 Module Summary.mp4 (1.81 MB)
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1 Module Overview.mp4 (1.76 MB)
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2 Local Receptive Fields.mp4 (4.28 MB)
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3 Understanding Convolution.mp4 (5.98 MB)
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4 Convolutional Layers.mp4 (11.06 MB)
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5 Pooling Layers.mp4 (6.38 MB)
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6 Typical CNN Architecture.mp4 (5.89 MB)
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7 Applying Convolutional and Pooling Layers.mp4 (17.28 MB)
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8 Module Summary.mp4 (1.77 MB)
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01 Module Overview.mp4 (2 MB)
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02 Zero Padding and Stride Size.mp4 (6.49 MB)
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03 Batch Normalization.mp4 (7.18 MB)
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04 Activation Functions.mp4 (3.72 MB)
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05 Feature Map Size Calculations.mp4 (3.18 MB)
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06 Preparing and Exploring Image Data.mp4 (7.83 MB)
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07 Setting up a Convolutional Neural Network.mp4 (11.25 MB)
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08 Training a CNN.mp4 (10.25 MB)
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09 Hyperparameter Tuning.mp4 (10.53 MB)
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10 Module Summary.mp4 (2.1 MB)
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1 Module Overview.mp4 (1.59 MB)
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2 Preparing the CIFAR-10 Dataset.mp4 (7.73 MB)
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3 Setting up the CNN.mp4 (6.92 MB)
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4 Training the CNN.mp4 (8.45 MB)
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5 Choosing Different Activation Functions.mp4 (7.7 MB)
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6 Choosing Pooling Layers.mp4 (7.2 MB)
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7 Choosing Convolution Kernel Sizes.mp4 (8.24 MB)
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8 Additional Convolution Layers and Different Kernel Size.mp4 (8.5 MB)
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9 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (1.81 MB)
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2 Transfer Learning.mp4 (7.72 MB)
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3 Using the Resnet-18 Pretrained Model.mp4 (12.04 MB)
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4 The Train Function to Find the Best Model Weights.mp4 (8.45 MB)
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5 Predictions Using Pretrained Models.mp4 (4.68 MB)
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6 Cleaning up Resources.mp4 (2.25 MB)
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7 Summary and Further Study.mp4 (2.13 MB)
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1 Course Overview.mp4 (3.85 MB)
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01 Version Check.mp4 (554.89 KB)
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02 Module Overview.mp4 (1.83 MB)
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03 Prerequisites and Course Outline.mp4 (2.3 MB)
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04 Content, Style, and Target Images.mp4 (7.26 MB)
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05 Training the Target Image for Style Transfer.mp4 (12.48 MB)
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06 Content Loss.mp4 (6.22 MB)
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07 Style Loss - Cosine Similarity and Dot Products.mp4 (5.37 MB)
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08 Style Loss - Gram Matrix.mp4 (5.75 MB)
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09 Setting up a Deep Learning Virtual Machine.mp4 (11.45 MB)
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10 Using Convolution Filters to Detect Features.mp4 (14.67 MB)
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11 Module Summary.mp4 (1.85 MB)
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1 Module Overview.mp4 (1.82 MB)
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2 Pretrained Models for Style Transfer.mp4 (3.82 MB)
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3 Loading the VGG19 Pretrained Model.mp4 (7.11 MB)
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4 Exploring and Transforming the Content and Style Images.mp4 (14.27 MB)
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5 Extracting Feature Maps from the Content and Style Images.mp4 (7.96 MB)
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6 Calculating the Gram Matrix to Extract Style Information.mp4 (5.89 MB)
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7 Training the Target Image to Perform Style Transfer.mp4 (12.44 MB)
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8 Style Transfer Using AlexNet.mp4 (14.74 MB)
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9 Module Summary.mp4 (1.01 MB)
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01 Module Overview.mp4 (2.06 MB)
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02 Understanding Generative Adversarial Networks (GANs).mp4 (9.46 MB)
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03 Training a GAN.mp4 (4.96 MB)
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04 Understanding the Leaky ReLU Activation Function.mp4 (8.79 MB)
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05 Loading and Exploring the MNIST Handwritten Digit Images.mp4 (9.01 MB)
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06 Setting up the Generator and Discriminator Neural Networks.mp4 (8.3 MB)
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07 Training the Discriminator.mp4 (9.39 MB)
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08 Training the Generator and Generating Fake Images.mp4 (7.97 MB)
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09 Cleaning up Resources.mp4 (2.66 MB)
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10 Summary and Further Study.mp4 (2.59 MB)
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1 Course Overview.mp4 (3.35 MB)
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1 Version Check.mp4 (560.2 KB)
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2 Module Overview.mp4 (1.99 MB)
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3 Prerequisites and Course Outline.mp4 (2.26 MB)
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4 RNNs for Natural Language Processing.mp4 (5.57 MB)
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5 Recurrent Neurons.mp4 (6.8 MB)
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6 Back Propagation through Time.mp4 (7.46 MB)
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7 Coping with Vanishing and Exploding Gradients.mp4 (9.61 MB)
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8 Long Memory Cells.mp4 (10.05 MB)
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9 Module Summary.mp4 (2.23 MB)
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01 Module Overview.mp4 (1.81 MB)
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02 Word Embeddings to Represent Text Data.mp4 (7.27 MB)
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03 Introducing torchtext to Process Text Data.mp4 (3.62 MB)
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04 Feeding Text Data into RNNs.mp4 (5.15 MB)
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05 Setup and Data Cleaning.mp4 (8.15 MB)
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06 Using Torchtext to Process Text Data.mp4 (18.78 MB)
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07 Designing an RNN for Binary Text Classification.mp4 (11.08 MB)
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08 Training the RNN.mp4 (10.96 MB)
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09 Using LSTM Cells and Dropout.mp4 (5.59 MB)
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10 Module Summary.mp4 (1.91 MB)
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1 Module Overview.mp4 (2.05 MB)
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2 Language Prediction Based on Names.mp4 (3.22 MB)
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3 Loading and Cleaning Data.mp4 (14.07 MB)
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4 Helper Functions to One Hot Encode Names.mp4 (6.32 MB)
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5 Designing an RNN for Multiclass Text Classification.mp4 (16.32 MB)
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6 Predicting Language from Names.mp4 (13.01 MB)
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7 Module Summary.mp4 (1.87 MB)
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01 Module Overview.mp4 (2.35 MB)
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02 Numeric Representations of Words.mp4 (4.39 MB)
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03 Word Embeddings Capture Context and Meaning.mp4 (6.44 MB)
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04 Generating Analogies Using GloVe Embeddings.mp4 (16.52 MB)
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05 Multilayer RNNs.mp4 (2.71 MB)
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06 Bidirectional RNNs.mp4 (6.71 MB)
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07 Data Cleaning and Preparation.mp4 (17.63 MB)
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08 Designing a Multilayer Bidirectional RNN.mp4 (11.32 MB)
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09 Performing Sentiment Analysis Using an RNN.mp4 (7.81 MB)
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10 Module Summary.mp4 (1.99 MB)
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01 Module Overview.mp4 (1.96 MB)
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02 Using Sequences and Vectors with RNNs.mp4 (5.44 MB)
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04 Representing Input and Target Sentences.mp4 (2.7 MB)
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05 Teacher Forcing.mp4 (4.96 MB)
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07 Preparing Sentence Pairs.mp4 (10.68 MB)
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08 Designing the Encoder and Decoder.mp4 (10.19 MB)
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10 Translating Sentences.mp4 (9.27 MB)
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11 Summary and Further Study.mp4 (3.16 MB)
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1 Course Overview.mp4 (3.43 MB)
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01 Version Check.mp4 (612.26 KB)
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02 Module Overview.mp4 (2.18 MB)
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03 Prerequisites and Course Outline.mp4 (1.99 MB)
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04 Introducing Transfer Learning.mp4 (7.2 MB)
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06 Categorizing Transfer Learning.mp4 (9.73 MB)
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07 Transfer Learning Scenarios.mp4 (7.92 MB)
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08 Freeze or Fine-tune Layers.mp4 (7.45 MB)
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09 Benefits of Transfer Learning.mp4 (3.66 MB)
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10 Pre-trained Models in PyTorch.mp4 (9.67 MB)
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12 Exploring Pre-trained Models in PyTorch.mp4 (21.1 MB)
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13 Module Summary.mp4 (1.9 MB)
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1 Module Overview.mp4 (2.66 MB)
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8 Fine-tuning Top Layers.mp4 (7.13 MB)
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9 Module Summary.mp4 (1.64 MB)
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1 Module Overview.mp4 (2.34 MB)
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2 Exploring and Loading the Chest X-Ray Dataset.mp4 (10.25 MB)
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3 Training a Model from Scratch.mp4 (10.19 MB)
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4 Exploring and Loading the Natural Images Dataset.mp4 (7.65 MB)
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5 Fine-tuning the Network.mp4 (8.92 MB)
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6 Cleaning up Resources.mp4 (2.54 MB)
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7 Summary and Further Study.mp4 (1.94 MB)
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1 Course Overview.mp4 (4.13 MB)
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01 Version Check.mp4 (575.06 KB)
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02 Prerequisites and Course Outline.mp4 (2.68 MB)
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03 Structural and Predictive Models.mp4 (8 MB)
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04 Demo - Install and Setup Pytorch.mp4 (8.18 MB)
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05 Demo - Preparing Data.mp4 (12.32 MB)
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06 Demo - Building a Simple Neural Network to Perform Regression.mp4 (11.7 MB)
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07 Demo - Exploring the Diamonds Dataset.mp4 (9.54 MB)
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08 Demo - Preparing and Processing Data.mp4 (10.25 MB)
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09 Demo - Building and Training a Regression Model.mp4 (16.7 MB)
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10 Demo - Exploring and Preprocessing Data.mp4 (15.6 MB)
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11 Demo - Defining the Neural Network and Helper Functions.mp4 (12.79 MB)
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1 Text as Sequential Data.mp4 (4.33 MB)
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2 The Recurrent Neuron.mp4 (5.18 MB)
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3 RNN Training and Long Memory Cells.mp4 (8.18 MB)
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4 RNN to Generate Names in Languages.mp4 (4.94 MB)
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5 Demo - Loading and Preparing Training Data.mp4 (11.14 MB)
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6 Demo - Setting up Helper Functions.mp4 (9.87 MB)
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7 Demo - Defining the RNN.mp4 (18.26 MB)
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8 Demo - Training the RNN and Generating Names.mp4 (16.41 MB)
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01 Finding Patterns in Data.mp4 (4.53 MB)
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02 Association Rule Learning.mp4 (3.6 MB)
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03 Clustering.mp4 (4.84 MB)
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04 Content Based Approaches to Recommendations.mp4 (7.14 MB)
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05 Collaborative Filtering.mp4 (5.66 MB)
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06 Nearest Neighborhood.mp4 (4 MB)
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07 Matrix Factorization.mp4 (9.28 MB)
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08 Alternating Least Squares to Estimate the Ratings Matrix.mp4 (5.8 MB)
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09 Evaluation Metrics vs Loss Metrics.mp4 (4.08 MB)
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10 Mean Average Precision @ K.mp4 (11.27 MB)
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11 Demo - Initializing the Ratings Matrix.mp4 (11.56 MB)
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12 Demo - Setting up the Neural Network.mp4 (12.31 MB)
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13 Demo - The Train Helper Function.mp4 (20.3 MB)
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14 Demo - The Evaluate Helper Function.mp4 (6.51 MB)
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16 Summary and Further Study.mp4 (2.12 MB)
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1 Course Overview.mp4 (3.78 MB)
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01 Version Check.mp4 (606.15 KB)
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02 Module Overview.mp4 (2.89 MB)
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03 Prerequisites and Course Outline.mp4 (1.88 MB)
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04 Saving and Loading PyTorch Models.mp4 (10.23 MB)
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05 Building and Training a Classifier Model.mp4 (11.85 MB)
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06 Saving and Loading Models Using torch save().mp4 (16.55 MB)
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07 Saving Model Using the state dict.mp4 (15.29 MB)
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08 Saving and Loading Checkpoints.mp4 (10.08 MB)
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09 Introducing ONNX.mp4 (2.76 MB)
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10 Exporting a Model to ONNX and Loading in Caffe2.mp4 (18.34 MB)
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11 Module Summary.mp4 (1.88 MB)
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1 Module Overview.mp4 (1.67 MB)
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3 Training Using Multiple Processes.mp4 (14.27 MB)
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5 Training on Multiple GPUs.mp4 (12.41 MB)
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6 Module Summary.mp4 (1.69 MB)
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1 Module Overview.mp4 (1.97 MB)
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2 Distributed Training on the Cloud.mp4 (5.48 MB)
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3 Setting up a SageMaker Notebook Instance.mp4 (8.88 MB)
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4 Setting up Training and Test Data Loaders.mp4 (9.46 MB)
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5 Define the Training Function.mp4 (9.4 MB)
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8 Module Summary.mp4 (1.54 MB)
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1 Module Overview.mp4 (1.78 MB)
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2 Exploring Options to Deploy PyTorch Models.mp4 (6.18 MB)
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4 Creating a Flask App to Serve the PyTorch Model.mp4 (10.87 MB)
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5 Using the Model for Prediction.mp4 (4.51 MB)
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6 Installing Docker.mp4 (5.56 MB)
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7 Creating and Using a Clipper Cluster for Prediction.mp4 (17.09 MB)
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9 Summary and Further Study.mp4 (2.01 MB)
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