2.08 GB | 00:07:03 | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English
Files Included :
1 Course Overview.mp4 (3.7 MB)
01 Version Check.mp4 (550.56 KB)
02 Module Overview.mp4 (1.3 MB)
03 Prerequisites and Course Outline.mp4 (2.8 MB)
04 Representation Learning Using Neural Networks.mp4 (10.35 MB)
05 Neuron as a Mathematical Function.mp4 (9.26 MB)
06 Activation Functions.mp4 (7.32 MB)
07 Introducing PyTorch.mp4 (5.73 MB)
08 TensorFlow and PyTorch.mp4 (6.75 MB)
09 Demo - PyTorch Install and Setup.mp4 (9.51 MB)
10 Summary.mp4 (1.4 MB)
01 Module Overview.mp4 (3.21 MB)
02 Demo - Creating and Initializing Tensors.mp4 (13.19 MB)
03 Demo - Simple Operations on Tensors.mp4 (10.82 MB)
04 Demo - Elementwise and Matrix Operations on Tensors.mp4 (7.69 MB)
05 Demo - Converting between PyTorch Tensors and NumPy Arrays.mp4 (8.39 MB)
06 PyTorch Support for CUDA Devices.mp4 (9.47 MB)
07 Demo - Setting up a Deep Learning VM to Work with GPUs.mp4 (15.47 MB)
08 Demo - Creating Tensors on CUDA-enabled Devices.mp4 (6.93 MB)
09 Demo - Working with the Device Context Manager.mp4 (9.23 MB)
10 Summary.mp4 (1.54 MB)
01 Module Overview.mp4 (1.54 MB)
02 Gradient Descent Optimization.mp4 (6.32 MB)
03 Forward and Backward Passes.mp4 (4.92 MB)
04 Calculating Gradients.mp4 (7.31 MB)
05 Using Gradients to Update Model Parameters.mp4 (5.9 MB)
06 Two Passes in Reverse Mode Automatic Differentiation.mp4 (5.99 MB)
07 Demo - Introducing Autograd.mp4 (10.08 MB)
08 Demo - Working with Gradients.mp4 (7.61 MB)
09 Demo - Variables and Tensors.mp4 (3.64 MB)
10 Demo - Training a Linear Model Using Autograd.mp4 (15.4 MB)
11 Summary.mp4 (2.07 MB)
01 Module Overview.mp4 (883.38 KB)
02 Static vs Dynamic Computation Graphs.mp4 (11.53 MB)
03 Dynamic Computation Graphs in PyTorch.mp4 (1.83 MB)
04 Demo - Installing Tensorflow, Graphviz, and Hidden Layer.mp4 (3.04 MB)
05 Demo - Building Dynamic Computations Graphs with PyTorch.mp4 (4.14 MB)
06 Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer.mp4 (5.35 MB)
07 Demo - Building Static Computation Graphs with Tensorflow.mp4 (11.05 MB)
08 Demo - Visualizing Tensorflow Graphs with Tensorboard.mp4 (3.92 MB)
09 Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution.mp4 (5.98 MB)
10 Debugging in PyTorch and Tensorflow.mp4 (2.13 MB)
11 Summary and Further Study.mp4 (2.27 MB)
1 Course Overview.mp4 (3.81 MB)
01 Version Check.mp4 (560.53 KB)
02 Module Overview.mp4 (1.63 MB)
03 Prerequisites and Course Outline.mp4 (1.99 MB)
04 CUDA Support in PyTorch.mp4 (10.08 MB)
05 Exploring PyTorch Install Options on a Local Machine.mp4 (4.38 MB)
06 Setting up a Virtual Machine.mp4 (9.41 MB)
07 Installing PyTorch with CPU Support Using Conda.mp4 (19.17 MB)
08 Installing PyTorch with CPU Support Using Pip.mp4 (10.37 MB)
09 Adding GPU Support to the VM and Installing the CUDA Toolkit.mp4 (15.1 MB)
10 Installing PyTorch with GPU Support Using Conda.mp4 (9.74 MB)
11 Installing PyTorch with CUDA Support Using Pip.mp4 (5.37 MB)
12 Module Summary.mp4 (1.87 MB)
1 Module Overview.mp4 (1.77 MB)
2 Linear Regression.mp4 (6.28 MB)
3 Finding the Best Fit Line.mp4 (5.23 MB)
4 Gradient Descent.mp4 (7.27 MB)
5 Training a Simple Neural Network with One Neuron.mp4 (12.18 MB)
7 Preventing Overfitting Using Regularization.mp4 (7.32 MB)
9 Module Summary.mp4 (2.3 MB)
01 Module Overview.mp4 (1.73 MB)
02 Training a Neural Network Forward and Backward Passes.mp4 (4.16 MB)
03 Optimizers.mp4 (5.76 MB)
04 Building a Neural Network Using PyTorch Layers.mp4 (10.46 MB)
05 Training a Neural Network Using Optimizers.mp4 (4.77 MB)
06 Dropout.mp4 (5.66 MB)
07 Epochs and Batches.mp4 (2.59 MB)
08 Exploring the Bike Sharing Dataset.mp4 (11.75 MB)
09 Using Datasets and Data Loaders in PyTorch.mp4 (5.38 MB)
11 Working with Different Neural Network Architectures.mp4 (9.11 MB)
12 Module Summary.mp4 (1.95 MB)
01 Module Overview.mp4 (1.77 MB)
02 Softmax and Cross Entropy.mp4 (6.67 MB)
03 Softmax and LogSoftmax.mp4 (4.48 MB)
04 Evaluating Classifiers.mp4 (3.28 MB)
05 Exploring the Graduate Admissions Dataset.mp4 (9.86 MB)
06 Preprocessing the Data.mp4 (8.19 MB)
07 Building a Custom Neural Network.mp4 (10.65 MB)
08 Training and Evaluating the Neural Network.mp4 (8 MB)
09 Customizing and Evaluating Different Models.mp4 (10.53 MB)
10 Summary and Further Study.mp4 (2.32 MB)
1 Course Overview.mp4 (2.92 MB)
01 Version Check.mp4 (577.35 KB)
02 Module Overview.mp4 (1.28 MB)
03 Prerequisites and Course Outline.mp4 (1.82 MB)
04 Machine Learning on the Cloud.mp4 (3.83 MB)
05 PyTorch - Taxonomy of Solutions.mp4 (4.76 MB)
06 Introducing SageMaker.mp4 (3.83 MB)
07 Creating a SageMaker Notebook Instance.mp4 (17.35 MB)
08 Prototyping a PyTorch Model on SageMaker Notebooks.mp4 (18.17 MB)
09 PyTorch Estimators on SageMaker.mp4 (2.34 MB)
10 Distributed Data Loading in PyTorch.mp4 (13.62 MB)
11 Distributed Training in PyTorch.mp4 (17.78 MB)
12 Using PyTorch Estimators for Distributed Training.mp4 (16.23 MB)
13 Model Deployment and Prediction Using Estimators.mp4 (10.28 MB)
14 AWS Deep Learning AMIs.mp4 (2.61 MB)
15 Instantiating a Deep Learning VM.mp4 (18.57 MB)
01 Module Overview.mp4 (1.42 MB)
02 Introducing Azure Machine Learning Service.mp4 (2.64 MB)
03 Prototyping PyTorch Models on Azure Notebooks.mp4 (16.01 MB)
04 Azure Machine Learning Service Workflow.mp4 (4.5 MB)
05 Understanding Terms in Azure Machine Learning.mp4 (3.81 MB)
06 Horovod for Distributed Training.mp4 (1.84 MB)
09 Distributed Run Using the PyTorch Estimator.mp4 (11.05 MB)
10 The Azure Deep Learning VM.mp4 (2.52 MB)
11 Instantiating an Azure Deep Learning VM.mp4 (15.53 MB)
1 Module Overview.mp4 (1004.95 KB)
2 Cloud Datalab and Deep Learning VMs.mp4 (4.11 MB)
3 Setting up a Cloud Datalab VM.mp4 (16.9 MB)
7 Summary and Further Study.mp4 (2.04 MB)
1 Course Overview.mp4 (3.44 MB)
01 Version Check.mp4 (564.49 KB)
02 Module Overview.mp4 (1.71 MB)
03 Prerequisites and Course Outline.mp4 (2.3 MB)
04 Single Channel and Multichannel Images.mp4 (6.47 MB)
05 Preprocessing Images to Train Robust Models.mp4 (8.46 MB)
06 Setting up a Deep Learning VM.mp4 (12.06 MB)
08 Cropping and Denoising Images.mp4 (11.21 MB)
09 Standardizing Images in PyTorch.mp4 (8.89 MB)
10 ZCA Whitening to Decorrelate Features.mp4 (5.5 MB)
11 Image Transformations Using PyTorch Libraries.mp4 (5.72 MB)
13 Module Summary.mp4 (1.81 MB)
1 Module Overview.mp4 (2.25 MB)
3 Loading and Processing MNIST Images.mp4 (11.78 MB)
6 Module Summary.mp4 (1.81 MB)
1 Module Overview.mp4 (1.76 MB)
2 Local Receptive Fields.mp4 (4.28 MB)
3 Understanding Convolution.mp4 (5.98 MB)
4 Convolutional Layers.mp4 (11.06 MB)
5 Pooling Layers.mp4 (6.38 MB)
6 Typical CNN Architecture.mp4 (5.89 MB)
7 Applying Convolutional and Pooling Layers.mp4 (17.28 MB)
8 Module Summary.mp4 (1.77 MB)
01 Module Overview.mp4 (2 MB)
02 Zero Padding and Stride Size.mp4 (6.49 MB)
03 Batch Normalization.mp4 (7.18 MB)
04 Activation Functions.mp4 (3.72 MB)
05 Feature Map Size Calculations.mp4 (3.18 MB)
06 Preparing and Exploring Image Data.mp4 (7.83 MB)
08 Training a CNN.mp4 (10.25 MB)
09 Hyperparameter Tuning.mp4 (10.53 MB)
10 Module Summary.mp4 (2.1 MB)
1 Module Overview.mp4 (1.59 MB)
2 Preparing the CIFAR-10 Dataset.mp4 (7.73 MB)
3 Setting up the CNN.mp4 (6.92 MB)
4 Training the CNN.mp4 (8.45 MB)
5 Choosing Different Activation Functions.mp4 (7.7 MB)
6 Choosing Pooling Layers.mp4 (7.2 MB)
7 Choosing Convolution Kernel Sizes.mp4 (8.24 MB)
9 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (1.81 MB)
2 Transfer Learning.mp4 (7.72 MB)
3 Using the Resnet-18 Pretrained Model.mp4 (12.04 MB)
4 The Train Function to Find the Best Model Weights.mp4 (8.45 MB)
5 Predictions Using Pretrained Models.mp4 (4.68 MB)
6 Cleaning up Resources.mp4 (2.25 MB)
7 Summary and Further Study.mp4 (2.13 MB)
1 Course Overview.mp4 (3.85 MB)
01 Version Check.mp4 (554.89 KB)
02 Module Overview.mp4 (1.83 MB)
03 Prerequisites and Course Outline.mp4 (2.3 MB)
04 Content, Style, and Target Images.mp4 (7.26 MB)
05 Training the Target Image for Style Transfer.mp4 (12.48 MB)
06 Content Loss.mp4 (6.22 MB)
07 Style Loss - Cosine Similarity and Dot Products.mp4 (5.37 MB)
08 Style Loss - Gram Matrix.mp4 (5.75 MB)
09 Setting up a Deep Learning Virtual Machine.mp4 (11.45 MB)
10 Using Convolution Filters to Detect Features.mp4 (14.67 MB)
11 Module Summary.mp4 (1.85 MB)
1 Module Overview.mp4 (1.82 MB)
2 Pretrained Models for Style Transfer.mp4 (3.82 MB)
3 Loading the VGG19 Pretrained Model.mp4 (7.11 MB)
4 Exploring and Transforming the Content and Style Images.mp4 (14.27 MB)
5 Extracting Feature Maps from the Content and Style Images.mp4 (7.96 MB)
6 Calculating the Gram Matrix to Extract Style Information.mp4 (5.89 MB)
7 Training the Target Image to Perform Style Transfer.mp4 (12.44 MB)
8 Style Transfer Using AlexNet.mp4 (14.74 MB)
9 Module Summary.mp4 (1.01 MB)
01 Module Overview.mp4 (2.06 MB)
02 Understanding Generative Adversarial Networks (GANs).mp4 (9.46 MB)
03 Training a GAN.mp4 (4.96 MB)
04 Understanding the Leaky ReLU Activation Function.mp4 (8.79 MB)
05 Loading and Exploring the MNIST Handwritten Digit Images.mp4 (9.01 MB)
06 Setting up the Generator and Discriminator Neural Networks.mp4 (8.3 MB)
07 Training the Discriminator.mp4 (9.39 MB)
08 Training the Generator and Generating Fake Images.mp4 (7.97 MB)
09 Cleaning up Resources.mp4 (2.66 MB)
10 Summary and Further Study.mp4 (2.59 MB)
1 Course Overview.mp4 (3.35 MB)
1 Version Check.mp4 (560.2 KB)
2 Module Overview.mp4 (1.99 MB)
3 Prerequisites and Course Outline.mp4 (2.26 MB)
4 RNNs for Natural Language Processing.mp4 (5.57 MB)
5 Recurrent Neurons.mp4 (6.8 MB)
6 Back Propagation through Time.mp4 (7.46 MB)
8 Long Memory Cells.mp4 (10.05 MB)
9 Module Summary.mp4 (2.23 MB)
01 Module Overview.mp4 (1.81 MB)
02 Word Embeddings to Represent Text Data.mp4 (7.27 MB)
03 Introducing torchtext to Process Text Data.mp4 (3.62 MB)
04 Feeding Text Data into RNNs.mp4 (5.15 MB)
05 Setup and Data Cleaning.mp4 (8.15 MB)
06 Using Torchtext to Process Text Data.mp4 (18.78 MB)
07 Designing an RNN for Binary Text Classification.mp4 (11.08 MB)
08 Training the RNN.mp4 (10.96 MB)
09 Using LSTM Cells and Dropout.mp4 (5.59 MB)
10 Module Summary.mp4 (1.91 MB)
1 Module Overview.mp4 (2.05 MB)
2 Language Prediction Based on Names.mp4 (3.22 MB)
3 Loading and Cleaning Data.mp4 (14.07 MB)
6 Predicting Language from Names.mp4 (13.01 MB)
7 Module Summary.mp4 (1.87 MB)
01 Module Overview.mp4 (2.35 MB)
02 Numeric Representations of Words.mp4 (4.39 MB)
03 Word Embeddings Capture Context and Meaning.mp4 (6.44 MB)
04 Generating Analogies Using GloVe Embeddings.mp4 (16.52 MB)
05 Multilayer RNNs.mp4 (2.71 MB)
06 Bidirectional RNNs.mp4 (6.71 MB)
07 Data Cleaning and Preparation.mp4 (17.63 MB)
08 Designing a Multilayer Bidirectional RNN.mp4 (11.32 MB)
09 Performing Sentiment Analysis Using an RNN.mp4 (7.81 MB)
10 Module Summary.mp4 (1.99 MB)
01 Module Overview.mp4 (1.96 MB)
05 Teacher Forcing.mp4 (4.96 MB)
07 Preparing Sentence Pairs.mp4 (10.68 MB)
10 Translating Sentences.mp4 (9.27 MB)
11 Summary and Further Study.mp4 (3.16 MB)
1 Course Overview.mp4 (3.43 MB)
01 Version Check.mp4 (612.26 KB)
02 Module Overview.mp4 (2.18 MB)
03 Prerequisites and Course Outline.mp4 (1.99 MB)
04 Introducing Transfer Learning.mp4 (7.2 MB)
06 Categorizing Transfer Learning.mp4 (9.73 MB)
07 Transfer Learning Scenarios.mp4 (7.92 MB)
08 Freeze or Fine-tune Layers.mp4 (7.45 MB)
09 Benefits of Transfer Learning.mp4 (3.66 MB)
10 Pre-trained Models in PyTorch.mp4 (9.67 MB)
13 Module Summary.mp4 (1.9 MB)
1 Module Overview.mp4 (2.66 MB)
9 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (2.34 MB)
3 Training a Model from Scratch.mp4 (10.19 MB)
5 Fine-tuning the Network.mp4 (8.92 MB)
6 Cleaning up Resources.mp4 (2.54 MB)
7 Summary and Further Study.mp4 (1.94 MB)
1 Course Overview.mp4 (4.13 MB)
01 Version Check.mp4 (575.06 KB)
02 Prerequisites and Course Outline.mp4 (2.68 MB)
03 Structural and Predictive Models.mp4 (8 MB)
04 Demo - Install and Setup Pytorch.mp4 (8.18 MB)
05 Demo - Preparing Data.mp4 (12.32 MB)
07 Demo - Exploring the Diamonds Dataset.mp4 (9.54 MB)
08 Demo - Preparing and Processing Data.mp4 (10.25 MB)
09 Demo - Building and Training a Regression Model.mp4 (16.7 MB)
10 Demo - Exploring and Preprocessing Data.mp4 (15.6 MB)
1 Text as Sequential Data.mp4 (4.33 MB)
2 The Recurrent Neuron.mp4 (5.18 MB)
3 RNN Training and Long Memory Cells.mp4 (8.18 MB)
4 RNN to Generate Names in Languages.mp4 (4.94 MB)
5 Demo - Loading and Preparing Training Data.mp4 (11.14 MB)
6 Demo - Setting up Helper Functions.mp4 (9.87 MB)
7 Demo - Defining the RNN.mp4 (18.26 MB)
8 Demo - Training the RNN and Generating Names.mp4 (16.41 MB)
01 Finding Patterns in Data.mp4 (4.53 MB)
02 Association Rule Learning.mp4 (3.6 MB)
03 Clustering.mp4 (4.84 MB)
04 Content Based Approaches to Recommendations.mp4 (7.14 MB)
05 Collaborative Filtering.mp4 (5.66 MB)
06 Nearest Neighborhood.mp4 (4 MB)
07 Matrix Factorization.mp4 (9.28 MB)
09 Evaluation Metrics vs Loss Metrics.mp4 (4.08 MB)
10 Mean Average Precision @ K.mp4 (11.27 MB)
11 Demo - Initializing the Ratings Matrix.mp4 (11.56 MB)
12 Demo - Setting up the Neural Network.mp4 (12.31 MB)
13 Demo - The Train Helper Function.mp4 (20.3 MB)
14 Demo - The Evaluate Helper Function.mp4 (6.51 MB)
16 Summary and Further Study.mp4 (2.12 MB)
1 Course Overview.mp4 (3.78 MB)
01 Version Check.mp4 (606.15 KB)
02 Module Overview.mp4 (2.89 MB)
03 Prerequisites and Course Outline.mp4 (1.88 MB)
04 Saving and Loading PyTorch Models.mp4 (10.23 MB)
05 Building and Training a Classifier Model.mp4 (11.85 MB)
07 Saving Model Using the state dict.mp4 (15.29 MB)
08 Saving and Loading Checkpoints.mp4 (10.08 MB)
09 Introducing ONNX.mp4 (2.76 MB)
11 Module Summary.mp4 (1.88 MB)
1 Module Overview.mp4 (1.67 MB)
6 Module Summary.mp4 (1.69 MB)
1 Module Overview.mp4 (1.97 MB)
8 Module Summary.mp4 (1.54 MB)
1 Module Overview.mp4 (1.78 MB)
2 Exploring Options to Deploy PyTorch Models.mp4 (6.18 MB)
5 Using the Model for Prediction.mp4 (4.51 MB)
6 Installing Docker.mp4 (5.56 MB)
9 Summary and Further Study.mp4 (2.01 MB)
01 Version Check.mp4 (550.56 KB)
02 Module Overview.mp4 (1.3 MB)
03 Prerequisites and Course Outline.mp4 (2.8 MB)
04 Representation Learning Using Neural Networks.mp4 (10.35 MB)
05 Neuron as a Mathematical Function.mp4 (9.26 MB)
06 Activation Functions.mp4 (7.32 MB)
07 Introducing PyTorch.mp4 (5.73 MB)
08 TensorFlow and PyTorch.mp4 (6.75 MB)
09 Demo - PyTorch Install and Setup.mp4 (9.51 MB)
10 Summary.mp4 (1.4 MB)
01 Module Overview.mp4 (3.21 MB)
02 Demo - Creating and Initializing Tensors.mp4 (13.19 MB)
03 Demo - Simple Operations on Tensors.mp4 (10.82 MB)
04 Demo - Elementwise and Matrix Operations on Tensors.mp4 (7.69 MB)
05 Demo - Converting between PyTorch Tensors and NumPy Arrays.mp4 (8.39 MB)
06 PyTorch Support for CUDA Devices.mp4 (9.47 MB)
07 Demo - Setting up a Deep Learning VM to Work with GPUs.mp4 (15.47 MB)
08 Demo - Creating Tensors on CUDA-enabled Devices.mp4 (6.93 MB)
09 Demo - Working with the Device Context Manager.mp4 (9.23 MB)
10 Summary.mp4 (1.54 MB)
01 Module Overview.mp4 (1.54 MB)
02 Gradient Descent Optimization.mp4 (6.32 MB)
03 Forward and Backward Passes.mp4 (4.92 MB)
04 Calculating Gradients.mp4 (7.31 MB)
05 Using Gradients to Update Model Parameters.mp4 (5.9 MB)
06 Two Passes in Reverse Mode Automatic Differentiation.mp4 (5.99 MB)
07 Demo - Introducing Autograd.mp4 (10.08 MB)
08 Demo - Working with Gradients.mp4 (7.61 MB)
09 Demo - Variables and Tensors.mp4 (3.64 MB)
10 Demo - Training a Linear Model Using Autograd.mp4 (15.4 MB)
11 Summary.mp4 (2.07 MB)
01 Module Overview.mp4 (883.38 KB)
02 Static vs Dynamic Computation Graphs.mp4 (11.53 MB)
03 Dynamic Computation Graphs in PyTorch.mp4 (1.83 MB)
04 Demo - Installing Tensorflow, Graphviz, and Hidden Layer.mp4 (3.04 MB)
05 Demo - Building Dynamic Computations Graphs with PyTorch.mp4 (4.14 MB)
06 Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer.mp4 (5.35 MB)
07 Demo - Building Static Computation Graphs with Tensorflow.mp4 (11.05 MB)
08 Demo - Visualizing Tensorflow Graphs with Tensorboard.mp4 (3.92 MB)
09 Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution.mp4 (5.98 MB)
10 Debugging in PyTorch and Tensorflow.mp4 (2.13 MB)
11 Summary and Further Study.mp4 (2.27 MB)
1 Course Overview.mp4 (3.81 MB)
01 Version Check.mp4 (560.53 KB)
02 Module Overview.mp4 (1.63 MB)
03 Prerequisites and Course Outline.mp4 (1.99 MB)
04 CUDA Support in PyTorch.mp4 (10.08 MB)
05 Exploring PyTorch Install Options on a Local Machine.mp4 (4.38 MB)
06 Setting up a Virtual Machine.mp4 (9.41 MB)
07 Installing PyTorch with CPU Support Using Conda.mp4 (19.17 MB)
08 Installing PyTorch with CPU Support Using Pip.mp4 (10.37 MB)
09 Adding GPU Support to the VM and Installing the CUDA Toolkit.mp4 (15.1 MB)
10 Installing PyTorch with GPU Support Using Conda.mp4 (9.74 MB)
11 Installing PyTorch with CUDA Support Using Pip.mp4 (5.37 MB)
12 Module Summary.mp4 (1.87 MB)
1 Module Overview.mp4 (1.77 MB)
2 Linear Regression.mp4 (6.28 MB)
3 Finding the Best Fit Line.mp4 (5.23 MB)
4 Gradient Descent.mp4 (7.27 MB)
5 Training a Simple Neural Network with One Neuron.mp4 (12.18 MB)
7 Preventing Overfitting Using Regularization.mp4 (7.32 MB)
9 Module Summary.mp4 (2.3 MB)
01 Module Overview.mp4 (1.73 MB)
02 Training a Neural Network Forward and Backward Passes.mp4 (4.16 MB)
03 Optimizers.mp4 (5.76 MB)
04 Building a Neural Network Using PyTorch Layers.mp4 (10.46 MB)
05 Training a Neural Network Using Optimizers.mp4 (4.77 MB)
06 Dropout.mp4 (5.66 MB)
07 Epochs and Batches.mp4 (2.59 MB)
08 Exploring the Bike Sharing Dataset.mp4 (11.75 MB)
09 Using Datasets and Data Loaders in PyTorch.mp4 (5.38 MB)
11 Working with Different Neural Network Architectures.mp4 (9.11 MB)
12 Module Summary.mp4 (1.95 MB)
01 Module Overview.mp4 (1.77 MB)
02 Softmax and Cross Entropy.mp4 (6.67 MB)
03 Softmax and LogSoftmax.mp4 (4.48 MB)
04 Evaluating Classifiers.mp4 (3.28 MB)
05 Exploring the Graduate Admissions Dataset.mp4 (9.86 MB)
06 Preprocessing the Data.mp4 (8.19 MB)
07 Building a Custom Neural Network.mp4 (10.65 MB)
08 Training and Evaluating the Neural Network.mp4 (8 MB)
09 Customizing and Evaluating Different Models.mp4 (10.53 MB)
10 Summary and Further Study.mp4 (2.32 MB)
1 Course Overview.mp4 (2.92 MB)
01 Version Check.mp4 (577.35 KB)
02 Module Overview.mp4 (1.28 MB)
03 Prerequisites and Course Outline.mp4 (1.82 MB)
04 Machine Learning on the Cloud.mp4 (3.83 MB)
05 PyTorch - Taxonomy of Solutions.mp4 (4.76 MB)
06 Introducing SageMaker.mp4 (3.83 MB)
07 Creating a SageMaker Notebook Instance.mp4 (17.35 MB)
08 Prototyping a PyTorch Model on SageMaker Notebooks.mp4 (18.17 MB)
09 PyTorch Estimators on SageMaker.mp4 (2.34 MB)
10 Distributed Data Loading in PyTorch.mp4 (13.62 MB)
11 Distributed Training in PyTorch.mp4 (17.78 MB)
12 Using PyTorch Estimators for Distributed Training.mp4 (16.23 MB)
13 Model Deployment and Prediction Using Estimators.mp4 (10.28 MB)
14 AWS Deep Learning AMIs.mp4 (2.61 MB)
15 Instantiating a Deep Learning VM.mp4 (18.57 MB)
01 Module Overview.mp4 (1.42 MB)
02 Introducing Azure Machine Learning Service.mp4 (2.64 MB)
03 Prototyping PyTorch Models on Azure Notebooks.mp4 (16.01 MB)
04 Azure Machine Learning Service Workflow.mp4 (4.5 MB)
05 Understanding Terms in Azure Machine Learning.mp4 (3.81 MB)
06 Horovod for Distributed Training.mp4 (1.84 MB)
09 Distributed Run Using the PyTorch Estimator.mp4 (11.05 MB)
10 The Azure Deep Learning VM.mp4 (2.52 MB)
11 Instantiating an Azure Deep Learning VM.mp4 (15.53 MB)
1 Module Overview.mp4 (1004.95 KB)
2 Cloud Datalab and Deep Learning VMs.mp4 (4.11 MB)
3 Setting up a Cloud Datalab VM.mp4 (16.9 MB)
7 Summary and Further Study.mp4 (2.04 MB)
1 Course Overview.mp4 (3.44 MB)
01 Version Check.mp4 (564.49 KB)
02 Module Overview.mp4 (1.71 MB)
03 Prerequisites and Course Outline.mp4 (2.3 MB)
04 Single Channel and Multichannel Images.mp4 (6.47 MB)
05 Preprocessing Images to Train Robust Models.mp4 (8.46 MB)
06 Setting up a Deep Learning VM.mp4 (12.06 MB)
08 Cropping and Denoising Images.mp4 (11.21 MB)
09 Standardizing Images in PyTorch.mp4 (8.89 MB)
10 ZCA Whitening to Decorrelate Features.mp4 (5.5 MB)
11 Image Transformations Using PyTorch Libraries.mp4 (5.72 MB)
13 Module Summary.mp4 (1.81 MB)
1 Module Overview.mp4 (2.25 MB)
3 Loading and Processing MNIST Images.mp4 (11.78 MB)
6 Module Summary.mp4 (1.81 MB)
1 Module Overview.mp4 (1.76 MB)
2 Local Receptive Fields.mp4 (4.28 MB)
3 Understanding Convolution.mp4 (5.98 MB)
4 Convolutional Layers.mp4 (11.06 MB)
5 Pooling Layers.mp4 (6.38 MB)
6 Typical CNN Architecture.mp4 (5.89 MB)
7 Applying Convolutional and Pooling Layers.mp4 (17.28 MB)
8 Module Summary.mp4 (1.77 MB)
01 Module Overview.mp4 (2 MB)
02 Zero Padding and Stride Size.mp4 (6.49 MB)
03 Batch Normalization.mp4 (7.18 MB)
04 Activation Functions.mp4 (3.72 MB)
05 Feature Map Size Calculations.mp4 (3.18 MB)
06 Preparing and Exploring Image Data.mp4 (7.83 MB)
08 Training a CNN.mp4 (10.25 MB)
09 Hyperparameter Tuning.mp4 (10.53 MB)
10 Module Summary.mp4 (2.1 MB)
1 Module Overview.mp4 (1.59 MB)
2 Preparing the CIFAR-10 Dataset.mp4 (7.73 MB)
3 Setting up the CNN.mp4 (6.92 MB)
4 Training the CNN.mp4 (8.45 MB)
5 Choosing Different Activation Functions.mp4 (7.7 MB)
6 Choosing Pooling Layers.mp4 (7.2 MB)
7 Choosing Convolution Kernel Sizes.mp4 (8.24 MB)
9 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (1.81 MB)
2 Transfer Learning.mp4 (7.72 MB)
3 Using the Resnet-18 Pretrained Model.mp4 (12.04 MB)
4 The Train Function to Find the Best Model Weights.mp4 (8.45 MB)
5 Predictions Using Pretrained Models.mp4 (4.68 MB)
6 Cleaning up Resources.mp4 (2.25 MB)
7 Summary and Further Study.mp4 (2.13 MB)
1 Course Overview.mp4 (3.85 MB)
01 Version Check.mp4 (554.89 KB)
02 Module Overview.mp4 (1.83 MB)
03 Prerequisites and Course Outline.mp4 (2.3 MB)
04 Content, Style, and Target Images.mp4 (7.26 MB)
05 Training the Target Image for Style Transfer.mp4 (12.48 MB)
06 Content Loss.mp4 (6.22 MB)
07 Style Loss - Cosine Similarity and Dot Products.mp4 (5.37 MB)
08 Style Loss - Gram Matrix.mp4 (5.75 MB)
09 Setting up a Deep Learning Virtual Machine.mp4 (11.45 MB)
10 Using Convolution Filters to Detect Features.mp4 (14.67 MB)
11 Module Summary.mp4 (1.85 MB)
1 Module Overview.mp4 (1.82 MB)
2 Pretrained Models for Style Transfer.mp4 (3.82 MB)
3 Loading the VGG19 Pretrained Model.mp4 (7.11 MB)
4 Exploring and Transforming the Content and Style Images.mp4 (14.27 MB)
5 Extracting Feature Maps from the Content and Style Images.mp4 (7.96 MB)
6 Calculating the Gram Matrix to Extract Style Information.mp4 (5.89 MB)
7 Training the Target Image to Perform Style Transfer.mp4 (12.44 MB)
8 Style Transfer Using AlexNet.mp4 (14.74 MB)
9 Module Summary.mp4 (1.01 MB)
01 Module Overview.mp4 (2.06 MB)
02 Understanding Generative Adversarial Networks (GANs).mp4 (9.46 MB)
03 Training a GAN.mp4 (4.96 MB)
04 Understanding the Leaky ReLU Activation Function.mp4 (8.79 MB)
05 Loading and Exploring the MNIST Handwritten Digit Images.mp4 (9.01 MB)
06 Setting up the Generator and Discriminator Neural Networks.mp4 (8.3 MB)
07 Training the Discriminator.mp4 (9.39 MB)
08 Training the Generator and Generating Fake Images.mp4 (7.97 MB)
09 Cleaning up Resources.mp4 (2.66 MB)
10 Summary and Further Study.mp4 (2.59 MB)
1 Course Overview.mp4 (3.35 MB)
1 Version Check.mp4 (560.2 KB)
2 Module Overview.mp4 (1.99 MB)
3 Prerequisites and Course Outline.mp4 (2.26 MB)
4 RNNs for Natural Language Processing.mp4 (5.57 MB)
5 Recurrent Neurons.mp4 (6.8 MB)
6 Back Propagation through Time.mp4 (7.46 MB)
8 Long Memory Cells.mp4 (10.05 MB)
9 Module Summary.mp4 (2.23 MB)
01 Module Overview.mp4 (1.81 MB)
02 Word Embeddings to Represent Text Data.mp4 (7.27 MB)
03 Introducing torchtext to Process Text Data.mp4 (3.62 MB)
04 Feeding Text Data into RNNs.mp4 (5.15 MB)
05 Setup and Data Cleaning.mp4 (8.15 MB)
06 Using Torchtext to Process Text Data.mp4 (18.78 MB)
07 Designing an RNN for Binary Text Classification.mp4 (11.08 MB)
08 Training the RNN.mp4 (10.96 MB)
09 Using LSTM Cells and Dropout.mp4 (5.59 MB)
10 Module Summary.mp4 (1.91 MB)
1 Module Overview.mp4 (2.05 MB)
2 Language Prediction Based on Names.mp4 (3.22 MB)
3 Loading and Cleaning Data.mp4 (14.07 MB)
6 Predicting Language from Names.mp4 (13.01 MB)
7 Module Summary.mp4 (1.87 MB)
01 Module Overview.mp4 (2.35 MB)
02 Numeric Representations of Words.mp4 (4.39 MB)
03 Word Embeddings Capture Context and Meaning.mp4 (6.44 MB)
04 Generating Analogies Using GloVe Embeddings.mp4 (16.52 MB)
05 Multilayer RNNs.mp4 (2.71 MB)
06 Bidirectional RNNs.mp4 (6.71 MB)
07 Data Cleaning and Preparation.mp4 (17.63 MB)
08 Designing a Multilayer Bidirectional RNN.mp4 (11.32 MB)
09 Performing Sentiment Analysis Using an RNN.mp4 (7.81 MB)
10 Module Summary.mp4 (1.99 MB)
01 Module Overview.mp4 (1.96 MB)
05 Teacher Forcing.mp4 (4.96 MB)
07 Preparing Sentence Pairs.mp4 (10.68 MB)
10 Translating Sentences.mp4 (9.27 MB)
11 Summary and Further Study.mp4 (3.16 MB)
1 Course Overview.mp4 (3.43 MB)
01 Version Check.mp4 (612.26 KB)
02 Module Overview.mp4 (2.18 MB)
03 Prerequisites and Course Outline.mp4 (1.99 MB)
04 Introducing Transfer Learning.mp4 (7.2 MB)
06 Categorizing Transfer Learning.mp4 (9.73 MB)
07 Transfer Learning Scenarios.mp4 (7.92 MB)
08 Freeze or Fine-tune Layers.mp4 (7.45 MB)
09 Benefits of Transfer Learning.mp4 (3.66 MB)
10 Pre-trained Models in PyTorch.mp4 (9.67 MB)
13 Module Summary.mp4 (1.9 MB)
1 Module Overview.mp4 (2.66 MB)
9 Module Summary.mp4 (1.64 MB)
1 Module Overview.mp4 (2.34 MB)
3 Training a Model from Scratch.mp4 (10.19 MB)
5 Fine-tuning the Network.mp4 (8.92 MB)
6 Cleaning up Resources.mp4 (2.54 MB)
7 Summary and Further Study.mp4 (1.94 MB)
1 Course Overview.mp4 (4.13 MB)
01 Version Check.mp4 (575.06 KB)
02 Prerequisites and Course Outline.mp4 (2.68 MB)
03 Structural and Predictive Models.mp4 (8 MB)
04 Demo - Install and Setup Pytorch.mp4 (8.18 MB)
05 Demo - Preparing Data.mp4 (12.32 MB)
07 Demo - Exploring the Diamonds Dataset.mp4 (9.54 MB)
08 Demo - Preparing and Processing Data.mp4 (10.25 MB)
09 Demo - Building and Training a Regression Model.mp4 (16.7 MB)
10 Demo - Exploring and Preprocessing Data.mp4 (15.6 MB)
1 Text as Sequential Data.mp4 (4.33 MB)
2 The Recurrent Neuron.mp4 (5.18 MB)
3 RNN Training and Long Memory Cells.mp4 (8.18 MB)
4 RNN to Generate Names in Languages.mp4 (4.94 MB)
5 Demo - Loading and Preparing Training Data.mp4 (11.14 MB)
6 Demo - Setting up Helper Functions.mp4 (9.87 MB)
7 Demo - Defining the RNN.mp4 (18.26 MB)
8 Demo - Training the RNN and Generating Names.mp4 (16.41 MB)
01 Finding Patterns in Data.mp4 (4.53 MB)
02 Association Rule Learning.mp4 (3.6 MB)
03 Clustering.mp4 (4.84 MB)
04 Content Based Approaches to Recommendations.mp4 (7.14 MB)
05 Collaborative Filtering.mp4 (5.66 MB)
06 Nearest Neighborhood.mp4 (4 MB)
07 Matrix Factorization.mp4 (9.28 MB)
09 Evaluation Metrics vs Loss Metrics.mp4 (4.08 MB)
10 Mean Average Precision @ K.mp4 (11.27 MB)
11 Demo - Initializing the Ratings Matrix.mp4 (11.56 MB)
12 Demo - Setting up the Neural Network.mp4 (12.31 MB)
13 Demo - The Train Helper Function.mp4 (20.3 MB)
14 Demo - The Evaluate Helper Function.mp4 (6.51 MB)
16 Summary and Further Study.mp4 (2.12 MB)
1 Course Overview.mp4 (3.78 MB)
01 Version Check.mp4 (606.15 KB)
02 Module Overview.mp4 (2.89 MB)
03 Prerequisites and Course Outline.mp4 (1.88 MB)
04 Saving and Loading PyTorch Models.mp4 (10.23 MB)
05 Building and Training a Classifier Model.mp4 (11.85 MB)
07 Saving Model Using the state dict.mp4 (15.29 MB)
08 Saving and Loading Checkpoints.mp4 (10.08 MB)
09 Introducing ONNX.mp4 (2.76 MB)
11 Module Summary.mp4 (1.88 MB)
1 Module Overview.mp4 (1.67 MB)
6 Module Summary.mp4 (1.69 MB)
1 Module Overview.mp4 (1.97 MB)
8 Module Summary.mp4 (1.54 MB)
1 Module Overview.mp4 (1.78 MB)
2 Exploring Options to Deploy PyTorch Models.mp4 (6.18 MB)
5 Using the Model for Prediction.mp4 (4.51 MB)
6 Installing Docker.mp4 (5.56 MB)
9 Summary and Further Study.mp4 (2.01 MB)
Screenshot
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!
Code:
Bitte
Anmelden
oder
Registrieren
um Code Inhalt zu sehen!