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Chapter 1, Neural Network Foundations with TensorFlow 2.0, this chapter will provide
a step-by-step introduction to neural networks. You will learn how to use tf.keras
layers in TensorFlow 2 to build simple neural network models. Perceptron, Multilayer
Perceptrons, Activation functions, and Dense Networks will be discussed.
Finally, the chapter provides an intuitive introduction to backpropagation.
Chapter 2, TensorFlow 1.x and 2.x, this chapter will compare TensorFlow 1.x and
TensorFlow 2.0 programming models. You will learn how to use TensorFlow
1.x lower-level computational graph APIs, and how to use tf.keras higher-level
APIs. New functionalities such as eager computation, Autograph, tf.Datasets,
and distributed training will be covered. Brief comparisons between tf.keras with
Estimators and between tf.keras and Keras will be provided.
Preface
Chapter 3, Regression, this chapter will focus on the most popular ML technique:
regression. You will learn how to use TensorFlow 2.0 estimators to build simple and
multiple regression models. You will learn to use logistic regression to solve a multiclass
classification problem.
Chapter 4, Convolutional Neural Networks, this chapter will introduce Convolutional
Neural Networks (CNNs) and their applications to image processing. You will
learn how to use TensorFlow 2.0 to build simple CNNs to recognize handwritten
characters in the MNIST dataset, and how to classify CIFAR images. Finally, you
will understand how to use pretrained networks such as VGG16 and Inception.
Chapter 5, Advanced Convolutional Neural Networks, this chapter discusses advanced
applications of CNNs to image, video, audio, and text processing. Examples of
image processing (Transfer Learning, DeepDream), audio processing (WaveNet),
and text processing (Sentiment Analysis, Q&A) will be discussed in detail.
Chapter 6, Generative Adversarial Networks, this chapter will focus on the recently
discovered Generative Adversarial Networks (GANs). We will start with the first
proposed GAN model and use it to forge MNIST characters. The chapter will use
deep convolutional GANs to create celebrity images. The chapter discusses the
various GAN architectures like SRGAN, InfoGAN, and CycleGAN. The chapter
covers a range of cool GAN applications. Finally, the chapter concludes with a
TensorFlow 2.0 implementation of CycleGAN to convert winter-summer images.
Chapter 7, Word Embeddings, this chapter will describe what word embeddings are,
with specific reference to two traditional popular embeddings: Word2vec and GloVe.
It will cover the core ideas behind these two embeddings and how to generate them
from your own corpus, as well as how to use them in your own networks for Natural
Language Processing (NLP) applications.
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