Book Image

Hands-On Neural Networks with TensorFlow 2.0

By : Paolo Galeone
Book Image

Hands-On Neural Networks with TensorFlow 2.0

By: Paolo Galeone

Overview of this book

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.
Table of Contents (15 chapters)
Free Chapter
1
Section 1: Neural Network Fundamentals
4
Section 2: TensorFlow Fundamentals
8
Section 3: The Application of Neural Networks

Exercises

Try answering and working on the following exercises to expand the knowledge that you've gained from this chapter:

  1. What is the adversarial training process?
  2. Write the value function of the min-max game that the Discriminator and Generator are playing.
  3. Explain why the min-max value function formulation can saturate in the early training step of training.
  4. Write and explain the non-saturating value function.
  5. Write the rules of the adversarial training process.
  6. Are there any recommendations on how to feed a condition to a GAN?
  7. What does it mean to create a conditional GAN?
  8. Can only the fully connected neural networks be used to create GANs?
  9. Which neural network architecture works better for the image generation problem?
  10. Update the code of the Unconditional GAN: Log the Generator and Discriminator loss value on TensorBoard, and also log matplotlib plots.
  11. Unconditional...