The
Deep
Learning
Workshop

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text.

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Nobody likes going through hundreds of pages of dry theory, or struggling with uninteresting examples that don’t compile. We've got you covered. Any time, any device.

Learn by doing real-world development, supported every step of the way with step-by-step examples and expert screencasts.
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Manage your learning based on your personal schedule, with content that lets you pause and resume your progress at will.

A Smarter Way to Learn DL

A step-by-step, focused approach to getting up and running with real-world deep learning in no time at all.

The Deep Learning Workshop is ideal if you're looking for a structured, hands-on approach to get started with deep learning.

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Course Curriculum

An A to Z tour of deep learning.

Workshop Onboarding
1. Building Blocks of Deep Learning
2. Neural Networks
- Overview
- Neural Networks and the Structure of Perceptrons
- Exercise 2.01: Perceptron Implementation
- Training a Perceptron
- Exercise 2.02: Perceptron as a Binary Classifier
- Multiclass Classifier
- Exercise 2.03: Multiclass Classification Using a Perceptron
- MNIST Case Study
- Exercise 2.04: Classifying Handwritten Digits
- Keras as a High-Level API
- Exercise 2.05: Binary Classification Using Keras
- Multilayer Neural Network or Deep Neural Network
- Exercise 2.06: Multilayer Binary Classifier
- Exercise 2.07: Deep Neural Network on MNIST Using Keras
- Exploring the Optimizers and Hyperparameters of Neural Networks
- Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
- Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
- Summary
- Survey I
3. Image Classification with Convolutional Neural Networks (CNNs)
- Overview
- Digital Images
- Exercise 3.01: Implementing a Convolution Operation
- Stride
- Exercise 3.02: Recognizing Handwritten Digits (MNIST) with CNN Using KERAS
- Data Generator
- Exercise 3.03: Classifying Cats versus Dogs with Data Generators
- Data Augmentation
- Exercise 3.04: Image Classification (CIFAR-10) with Data Augmentation
- Activity 3.01: Building a Multiclass Classifier Based on the Fashion MNIST Dataset
- Activity 3.01: Building a Multiclass Classifier Based on the Fashion MNIST Dataset
- Saving and Restoring Models
- Transfer Learning
- Activity 3.02: Fruit Classification with Transfer Learning
- Activity 3.02: Fruit Classification with Transfer Learning
- Summary
4. Deep Learning for Text – Embeddings
- Overview
- Deep Learning for Natural Language Processing
- Getting Started with Text Data Handling
- Exercise 4.01: Tokenizing, Case Normalization, Punctuation, and Stop Word Removal
- Stemming and Lemmatization
- Exercise 4.02: Stemming Our Data
- Beyond Stemming and Lemmatization
- Activity 4.01: Text Preprocessing of the 'Alice in Wonderland' Text
- Activity 4.01: Text Preprocessing of the 'Alice in Wonderland' Text
- Text Representation Considerations
- Exercise 4.03: Creating One-Hot Encoding for Our Data
- Term Frequencies
- Exercise 4.04: Document-Term Matrix with TF-IDF
- Summarizing the Classical Approaches
- Word Embeddings and Word Vectors
- Exercise 4.05: Vectors for Phrases
- Effect of Parameters – "size" of the Vector
- Exercise 4.06: Training Word Vectors on Different Datasets
- Using Pre-Trained Word Vectors
- Activity 4.02: Text Representation for Alice in Wonderland
- Activity 4.02: Text Representation for Alice in Wonderland
- Summary
- Survey II
5. Deep Learning for Sequences
- Overview
- Working with Sequences
- Exercise 5.01: Visualizing Our Time-Series Data
- Recurrent Neural Networks
- Exercise 5.02: Implementing the Forward Pass of a Simple RNN Using TensorFlow
- The Flexibility and Versatility of RNNs
- Preparing the Data for Stock Price Prediction
- Exercise 5.03: Building Our First Plain RNN Model
- Model Training and Performance Evaluation
- Exercise 5.04: Building a 1D Convolution-Based Model
- Performance of 1D Convnets
- Exercise 5.05: Building a Hybrid (1D Convolution and RNN) Model
- Activity 5.01: Using a Plain RNN Model to Predict IBM Stock Prices
- Activity 5.01: Using a Plain RNN Model to Predict IBM Stock Prices
- Summary
6. LSTMs, GRUs, and Advanced RNNs
- Overview
- Long-Range Dependence/Influence
- Sequence Models for Text Classification
- The Embedding Layer
- Exercise 6.01: Building and Training an RNN Model for Sentiment Classification
- Making Predictions on Unseen Data
- LSTMs, GRUs, and Other Variants
- Exercise 6.02: LSTM-Based Sentiment Classification Model
- LSTM versus Plain RNNs
- Gated Recurrence Units
- Exercise 6.03: GRU-Based Sentiment Classification Model
- LSTM versus GRU
- Bidirectional RNNs
- Exercise 6.04: Bidirectional LSTM-Based Sentiment Classification Model
- Stacked RNNs
- Exercise 6.05: Stacked LSTM-Based Sentiment Classification Model
- Summarizing All the Models
- Activity 6.01: Sentiment Analysis of Amazon Product Reviews
- Activity 6.01: Sentiment Analysis of Amazon Product Reviews
- Summary
- Survey III
7. Generative Adversarial Networks
- Overview
- Key Components of Generative Adversarial Networks
- Process 1 – Generating Real Data from the Known Function
- Exercise 7.01: Generating a Data Distribution from a Known Function
- Process 2 – Creating a Basic Generative Network
- Sequential()
- Exercise 7.02: Building a Generative Network
- Setting the Stage for the Discriminator Network
- Process 3 – Discriminator Network
- Exercise 7.03: Implementing the Discriminator Network
- Process 4 – Implementing the GAN
- Exercise 7.04: Implementing the GAN
- Deep Convolutional GANs
- Generating Handwritten Images Using DCGANs
- Exercise 7.05: Implementing the DCGAN
- Analysis of Sample Plots
- Common Problems with GANs
- Activity 7.01: Implementing a DCGAN for the MNIST Fashion Dataset
- Activity 7.01: Implementing a DCGAN for the MNIST Fashion Dataset
- Summary

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