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

Answering the following questions is of extreme importance: you are building your ML foundations—do not skip this step!

  1. Given a dataset of 1,000 labeled examples, what do you have to do if you want to measure the performance of a supervised learning algorithm during the training, validation, and test phases, while using accuracy as the unique metric?
  2. What is the difference between supervised and unsupervised learning?
  3. What is the difference between precision and recall?
  4. A model in a high-recall regime produces more or less false positives than a model in a low recall regime?
  5. Can the confusion matrix only be used in a binary classification problem? If not, how can we use it in a multiclass classification problem?
  6. Is one-class classification a supervised learning problem? If yes, why? If no, why?
  7. If a binary classifier has an AUC of 0.5, what can you conclude from...