Chapter 1: Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks
Setting up a deep learning environment
Launching an instance on Amazon Web Services (AWS)
Launching an instance on Google Cloud Platform (GCP)
Installing CUDA and cuDNN
Installing Anaconda and libraries
Connecting with Jupyter Notebooks on a server
Building state-of-the-art, production-ready models with TensorFlow
Intuitively building networks with Keras
Using PyTorch’s dynamic computation graphs for RNNs
Implementing high-performance models with CNTK
Building efficient models with MXNet
Defining networks using simple and efficient code with Gluon
Chapter 2: Feed-Forward Neural Networks
Understanding the perceptron
Implementing a single-layer neural network
Building a multi-layer neural network
Getting started with activation functions
Experiment with hidden layers and hidden units
Implementing an autoencoder
Experimenting with different optimizers
Improving generalization with regularization
Adding dropout to prevent overfitting
Chapter 3: Convolutional Neural Networks
Optimizing with batch normalization
Understanding padding and strides
Experimenting with different types of initialization
Implementing a convolutional autoencoder
Applying a 1D CNN to text
Chapter 4: Recurrent Neural Networks
Implementing a simple RNN
Adding Long Short-Term Memory (LSTM)
Using gated recurrent units (GRUs)
Implementing bidirectional RNNs
Character-level text generation
Chapter 5: Reinforcement Learning
Implementing policy gradients
Implementing a deep Q-learning algorithm
Chapter 6: Generative Adversarial Networks
Implementing Deep Convolutional GANs (DCGANs)
Upscaling the resolution of images with Super-Resolution GANs (SRGANs)
Chapter 7: Computer Vision
Augmenting images with computer vision techniques
Classifying objects in images
Localizing an object in images
Segmenting classes in images with U-net
Scene understanding (semantic segmentation)
Finding facial key points
Transferring styles to images
Chapter 8: Natural Language Processing
Chapter 9: Speech Recognition and Video Analysis
Implementing a speech recognition pipeline from scratch
Identifying speakers with voice recognition
Understanding videos with deep learning
Chapter 10: Time Series and Structured Data
Predicting stock prices with neural networks
Predicting bike sharing demand
Using a shallow neural network for binary classification
Chapter 11: Game Playing Agents and Robotics
Learning to drive a car with end-to-end learning
Learning to play games with deep reinforcement learning
Genetic Algorithm (GA) to optimize hyperparameters
Chapter 12: Hyperparameter Selection, Tuning, and Neural Network Learning
Visualizing training with TensorBoard and Keras
Working with batches and mini-batches
Using grid search for parameter tuning
Learning rates and learning rate schedulers
Determining the depth of the network
Adding dropouts to prevent overfitting
Making a model more robust with data augmentation
Chapter 13: Network Internals
Visualizing training with TensorBoard
Analyzing network weights and more
Storing the network topology and trained weights
Chapter 14: Pretrained Models
Large-scale visual recognition with GoogLeNet/Inception
Extracting bottleneck features with ResNet
Leveraging pretrained VGG models for new classes
Fine-tuning with Xception
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