Artificial Intelligence for Scientists and Engineers, Volume 2

Volume II – Deep Learning 1. Introduction to Artificial Neural Networks with Tensorflow and Keras 1. From biological to artificial neurons: Biological neurons, logical computations with neurons, the perceptron, the multilayer perceptron and backpropagation, MLPs for regression, MLPs for classification. 2. Implementing MLPs with Tensorflow and Keras: Installing Tensorflow and Keras, building an image classifier using the Sequential API, building a regression MLP using the Sequential API, building complex Volume II – Deep Learning 1. Introduction to Artificial Neural Networks with Tensorflow and Keras 1. From biological to artificial neurons: Biological neurons, logical computations with neurons, the perceptron, the multilayer perceptron and backpropagation, MLPs for regression, MLPs for classification. 2. Implementing MLPs with Tensorflow and Keras: Installing Tensorflow and Keras, building an image classifier using the Sequential API, building a regression MLP using the Sequential API, building complex 8. Classification and Localization 9. Object Detection: Fully convolutional networks, You Only Look Once (YOLO) 10. Semantic Segmentation 6. Processing Sequences Using RNNs and LSTMs 1. Recurrent Neurons and Layers: Memory cells, input and output sequences 2. Training RNNs 3. Forecasting a Time Series: baseline metrics, implementing a simple RNNs, deep RNNs, forecasting several time steps ahead 4. Handling Long Sequences: fighting the unstable gradients problem, tackling the short-term memory problem. 7. Natural Language Processing with RNNs 1. Generating Text Using a Character RNN: creating the training dataset, how to split a sequential dataset, chopping the sequential dataset into multiple windows, building and training the char-RNN model, using the char-RNN model, generating fake text, stateful RNN. 2. Sentiment Analysis: masking, reusing pre-trained embedding 3. An Encoder-Decoder Network for Neural Machine Translation: bidirectional RNNs, beam search. 4. Attention Mechanisms: visual attention, the transformer architecture for attention. 5. Recent Innovations in Language Models. 8. Representation Learning and Generative Learning Using Autoencoders and GANs 1. Efficient Data Representation 2. Performing PCA with an Undercomplete Linear Autoencoder 3. Stacked Autoencoder: implementing a stacked autoencoder using Keras, visualizing the reconstructions, visualizing the fashion MNIST dataset, unsupervised pretraining using attacked autoencoders, tying weights, training one autoencoder at a time. 4. Convolutional Autoencoders 5. Recurrent Autoencoders 6. Denoising Autoencoders 7. Sparse Autoencoders 8. Variational Autoencoders: generating fashion MNIST images 9. Generative Adversarial Networks: the difficulties of training GANS, deep convolutional GANs, progressive growing of GANs, StyleGANs. 9. Reinforcement Learning 1. Learning to Optimize Rewards 2. Policy Search 3. Introduction to OpenAI Gym 4. Neural Network Policies 5. Evaluating Actions: The Credit Assignment Problem 6. Policy Gradients 7. Markov Decision Processes 8. Temporal Difference Learning 9. Q-Learning: exploration policies, approximate Q-learning and deep Q�learning 10. Implementing Deep Q-Learning 11. Deep Q-Learning Variants: fixed Q-value targets, double DQN, prioritized experience replay, dueling DQN 12. The TF-Agents Library: installing TF-agents, TF-agents environments, environment specifications, environment wrappers and Atari preprocessing, training architecture, creating the deep Q-network, creating the DQN agent, creating the replay buffer and the corresponding observer, creating training metrics, creating training metrics, creating the collect driver, creating the dataset, creating the training loop. 13. Overview of Some Popular RL Algorithms 10. Training and Deploying Tensorflow Models at Scale 1. Serving a Tensorflow Model: using a Tensorflow model, creating a prediction service on the GCP AI platform, using the prediction service 2. Deploying Model to a Mobile or Embedded Device 3. Using GPUs to Speed Up Computations: getting the GPU, using a GPU�equipped virtual machine, Google Collaboratory, managing GPU RAM, placing operations and variables on devices, parallel execution across multiple devices. 4. Training Models across Multiple Devices: model parallelism, data parallelism, training at scale using distribution strategies API, training a model on a Tensorflow cluster, running large training jobs on Google cloud AI platform, black-box hyperparameter tuning AI platform