Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

Chapter 1: Optimization and neural networks
Subtopics: How to read the book Introduction to the book
Chapter 2: Hands-on with One Single NeuronSubtopics: Overview of optimization A definition of learning Constrained vs. unconstrained optimization Absolute and local minima Optimization algorithms with focus on Gradient Descent Variations of Gradient Descent (mini-batch and stochastic) How to choose the right mini-batch size
Chapter 3: Feed Forward Neural NetworksSubtopics: A short introduction to matrix algebra Activation functions (identity, sigmoid, tanh, swish, etc.) Implementation of one neuron in Keras Linear regression with one neuron Logistic regression with one neuron
Chapter 4: RegularizationSubtopics: Matrix formalism Softmax activation function Overfitting and bias-variance discussion How to implement a fully conneted network with Keras Multi-class classification with the Zalando dataset in Keras Gradient descent variation in practice with a real dataset Weight initialization How to compare the complexity of neural networks How to estimate memory used by neural networks in Keras
Chapter 5: Advanced OptimizersSubtopics: An introduction to regularization l_p norm l_2 regularization Weight decay when using regularization Dropout Early Stopping

Chapter 6Chapter Title: Hyper-Parameter tuningSubtopics: Exponentially weighted averages Momentum RMSProp Adam Comparison of optimizers
Chapter 7Chapter Title: Convolutional Neural NetworksSubtopics: Introduction to Hyper-parameter tuning Black box optimization Grid Search Random Search Coarse to fine optimization Sampling on logarithmic scale Bayesian optimisation
Chapter 8Chapter Title: Brief Introduction to Recurrent Neural NetworksSubtopics: Theory of convolution Pooling and padding Building blocks of a CNN Implementation of a CNN with Keras Introduction to recurrent neural networks Implementation of a RNN with Keras

Chapter 9: AutoencodersSubtopics: Feed Forward Autoencoders Loss function in autoencoders Reconstruction error Application of autoencoders: dimensionality reduction Application of autoencoders: Classification with latent features Curse of dimensionality Denoising autoencoders Autoencoders with CNN
Chapter 10: Metric AnalysisSubtopics: Human level performance and Bayes error Bias Metric analysis diagram Training set overfitting How to split your dataset Unbalanced dataset: what can happen K-fold cross validation Manual metric analysis: an example
Chapter 11 Chapter Title: General Adversarial Networks (GANs)Subtopics: Introduction to GANs The building blocks of GANs An example of implementation of GANs in Keras
APPENDIX 1: Introduction to KerasSubtopics: Sequential model Keras Layers Funct