Deep Learning A - Z (2025) Neural NetWorks, AI & ChatGPT Prize [2025]

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4.65 GB | 22min 58s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 (34.64 MB)
002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 (8.1 MB)
003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 (56.61 MB)
004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 (31.51 MB)
005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 (29.68 MB)
006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 (49.23 MB)
007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 (34.24 MB)
008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 (33.18 MB)
009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 (20.35 MB)
002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 (36.33 MB)
004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 (69.12 MB)
005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 (54.85 MB)
006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 (45.41 MB)
007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 (61.64 MB)
002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 (10.68 MB)
003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 (54.97 MB)
004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 (44.4 MB)
005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 (25.18 MB)
006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 (55.67 MB)
007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 (6.14 MB)
008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 (52.78 MB)
009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 (16.4 MB)
010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 (67.3 MB)
002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 (27.8 MB)
003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 (67.42 MB)
004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 (68.02 MB)
005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 (27.99 MB)
006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 (56.64 MB)
007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 (87.16 MB)
002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 (6.88 MB)
003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 (43.59 MB)
004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 (54.89 MB)
005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 (75.06 MB)
006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 (64.06 MB)
007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 (13.76 MB)
002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 (24.65 MB)
003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 (26.81 MB)
004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 (22.62 MB)
005 Step 4 - Building X train and y train Arrays for LSTM Time Series Forecasting.mp4 (57.77 MB)
006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 (41.4 MB)
007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 (10.81 MB)
008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 (33.05 MB)
009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 (20.26 MB)
010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 (12.68 MB)
011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 (16.56 MB)
012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 (41.48 MB)
013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 (21.26 MB)
014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 (64.1 MB)
015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 (31.64 MB)
016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 (34.33 MB)
001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 (9.5 MB)
002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 (32.54 MB)
003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 (7.14 MB)
004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 (53.79 MB)
005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 (38.91 MB)
006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 (25.35 MB)
007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 (16.99 MB)
008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 (54.69 MB)
009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 (29.66 MB)
010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 (43.17 MB)
002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 (52 MB)
003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 (36.64 MB)
004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 (64.29 MB)
005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 (44.87 MB)
002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 (10.74 MB)
003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 (17.78 MB)
004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 (55.65 MB)
005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 (35.35 MB)
001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 (6.5 MB)
002 Boltzmann Machines vs Neural Networks Key Differences in Deep Learning.mp4 (54.47 MB)
003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 (40.46 MB)
004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 (13.32 MB)
005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 (47.56 MB)
006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 (59.23 MB)
007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 (20.47 MB)
008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 (11.2 MB)
002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 (34.79 MB)
004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 (35.1 MB)
005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 (36.67 MB)
006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 (31.89 MB)
007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 (79.37 MB)
008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 (19.37 MB)
009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 (29.11 MB)
010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 (38.87 MB)
011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 (48.35 MB)
012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 (23.88 MB)
013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 (44.32 MB)
014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 (27.04 MB)
015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 (51.02 MB)
016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 (73.76 MB)
017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 (65.13 MB)
001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 (8.34 MB)
002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 (25.64 MB)
003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 (4.8 MB)
004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 (23.48 MB)
005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 (14.77 MB)
006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 (23.69 MB)
007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 (9.65 MB)
008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 (9.08 MB)
009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 (7.21 MB)
010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 (7.07 MB)
003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 (41.61 MB)
004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 (40.63 MB)
005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 (28.89 MB)
007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 (72.17 MB)
008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 (17.56 MB)
009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 (58.44 MB)
010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 (47.82 MB)
011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 (51.87 MB)
012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 (46.64 MB)
013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 (15.25 MB)
014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 (40.13 MB)
015 THANK YOU Video.mp4 (9.18 MB)
002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 (16.29 MB)
003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 (10.86 MB)
004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 (3.33 MB)
005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 (58.02 MB)
002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 (5.29 MB)
003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 (7.02 MB)
004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 (16.54 MB)
001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 (18.4 MB)
002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 (18.44 MB)
003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 (12.25 MB)
004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 (17.97 MB)
005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 (16.19 MB)
006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 (19.83 MB)
008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 (20.39 MB)
009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 (20.54 MB)
010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 (15.17 MB)
011 Step 2 - Using fit transform Method for Efficient Data Preprocessing in Python.mp4 (20.27 MB)
012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 (16.03 MB)
013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 (13.46 MB)
014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train test split.mp4 (20.58 MB)
015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 (13.33 MB)
016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 (20.45 MB)
017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 (16.34 MB)
018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 (13.1 MB)
019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 (20.17 MB)
001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 (16.91 MB)
002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 (9.66 MB)
003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 (19.65 MB)
004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 (13.71 MB)
005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 (20.11 MB)
006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 (20.46 MB)
007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 (13.67 MB)
008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 (12.03 MB)
009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 (20.56 MB)
010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 (6.27 MB)
011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 (20.42 MB)
012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 (20.2 MB)
013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 (11.46 MB)
014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 (20.29 MB)
015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 (12.84 MB)
016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 (11.48 MB)

]
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Deep Learning A-Z 2025: Neural Networks, AI & ChatGPT Prize
2025-01-25
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 9.31 GB | Duration: 22h 30m​

Learn to create Deep Learning models in Python from two Machine Learning, Data Science experts. Code templates included.

What you'll learn
Understand the intuition behind Artificial Neural Networks
Apply Artificial Neural Networks in practice
Understand the intuition behind Convolutional Neural Networks
Apply Convolutional Neural Networks in practice
Understand the intuition behind Recurrent Neural Networks
Apply Recurrent Neural Networks in practice
Understand the intuition behind Self-Organizing Maps
Apply Self-Organizing Maps in practice
Understand the intuition behind Boltzmann Machines
Apply Boltzmann Machines in practice
Understand the intuition behind AutoEncoders
Apply AutoEncoders in practice

Requirements
High school mathematics level
Basic Python programming knowledge

Description
*** As seen on Kickstarter ***Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

-- Why Deep Learning A-Z? --

Here are five reasons we think Deep Learning A-Z really is different, and stands out from the crowd of other training programs out there:

1. ROBUST STRUCTURE

The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.

That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.

2. INTUITION TUTORIALS

So many courses and books just bombard you with the theory, and math, and coding. But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.

With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.

3. EXCITING PROJECTS

Are you tired of courses based on over-used, outdated data sets?

Yes? Well then you're in for a treat.

Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

Artificial Neural Networks to solve a Customer Churn problem

Convolutional Neural Networks for Image Recognition

Recurrent Neural Networks to predict Stock Prices

Self-Organizing Maps to investigate Fraud

Boltzmann Machines to create a Recomender System

Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize

*Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.

4. HANDS-ON CODING

In Deep Learning A-Z we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.

This is a course which naturally extends into your career.

5. IN-COURSE SUPPORT

Have you ever taken a course or read a book where you have questions but cannot reach the author?

Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help.

In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum.

No matter how complex your query, we will be there. The bottom line is we want you to succeed.

-- The Tools --

Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both!

TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.

PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.

So which is better and for what?

Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.

The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques.

-- More Tools --

Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it.

Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing.

-- Even More Tools --

Scikit-learn the most practical Machine Learning library. We will mainly use it:

to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation

to improve our models with effective Parameter Tuning

to preprocess our data, so that our models can learn in the best conditions

And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience.

Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.

-- Who Is This Course For? --

As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z your skills are on the cutting edge of today's technology. If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident. If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications. -- Real-World Case Studies --

Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges:

#1 Churn Modelling Problem

In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.

Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.

If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.

#2 Image Recognition

In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it - by simply changing the pictures in the input folder.

For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin's dog!

#3 Stock Price Prediction

In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to "Artificial Intelligence". Why is that? Because this model will have long-term memory, just like us, humans.

The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course!

In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.

#4 Fraud Detection

According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That's why we have included this case study in the course.

This is the first part of Volume 2 - Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.

This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.

#5 & 6 Recommender Systems

From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet.

We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply "Liked" or "Not Liked".

Your final Recommender System will be able to predict the ratings of the movies the customers didn't watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models.

Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.

And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!

-- Summary --

In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies.

We are super enthusiastic about Deep Learning and hope to see you inside the class!

Kirill & Hadelin

Who this course is for:
Anyone interested in Deep Learning, Students who have at least high school knowledge in math and who want to start learning Deep Learning, Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning, Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets, Any students in college who want to start a career in Data Science, Any data analysts who want to level up in Deep Learning, Any people who are not satisfied with their job and who want to become a Data Scientist, Any people who want to create added value to their business by using powerful Deep Learning tools, Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business, Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms

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