Chapter 1, Maths for Neural Networks, covers the basics of algebra, probability, and optimization techniques for neural networks.
Chapter 2, Deep Feedforward Networks, explains the basics of perceptrons, neurons, and feedforward neural networks. You will also learn about various learning techniques and mainly the core learning algorithm called backpropagation.
Chapter 3, Optimization for Neural Networks, covers optimization techniques that are fundamental to neural network learning.
Chapter 4, Convolutional Neural Networks, discusses the CNN algorithm in detail. CNNs and their application to different data types will also be covered.
Chapter 5, Recurrent Neural Networks, covers the RNN algorithm in detail. RNNs and their application to different data types are covered as well.
Chapter 6, Generative Models, explains the basics of generative models and the different approaches to generative models.
Chapter 7, Deep Belief Networking, covers the basics of deep belief networks, how they differ from the traditional neural networks, and their implementation.
Chapter 8, Autoencoders, provides an introduction to autoencoders, which have recently come to the forefront of generative modeling.
Chapter 9, Deep Learning Research and Summary, discusses the current and future research details on deep learning. It also points the readers to papers for reference reading.
Appendix, Getting Started with TensorFlow, discusses environment setup of TensorFlow, comparison of TensorFlow with NumPy, and the concept if Auto differentiation
