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Beginning with Deep Learning Using TensorFlow A Beginners Guide to TensorFlow and Keras for Practicing Deep Learning Principles [#436371]
English | 2022 | ISBN: 9355510470 | 315 pages | PDF | 14.76 MB
A Practising Guide to TensorFlow and Deep Learning
Key Features
● Equipped with a necessary introduction to Deep Learning and AI.
● Includes demos and templates to give your projects a good start.
● Find more on the most important facets of AI, image, and speech recognition.
Description
This book begins with the configuration of an Anaconda development environment, essential for practicing the deep learning process. The basics of machine learning, which are needed for Deep Learning, are explained in this book.
TensorFlow is the industry-standard library for Deep Learning, and thereby, it is covered extensively with both versions, 1.x and 2.x. As neural networks are the heart of Deep Learning, the book explains them in great detail and systematic fashion, beginning with a single neuron and progressing through deep multilayer neural networks. The emphasis of this book is on the practical application of key concepts rather than going in-depth with them.
After establishing a firm basis in TensorFlow and Neural Networks, the book explains the concepts of image recognition using Convolutional Neural Networks (CNN), followed by speech recognition, and natural language processing (NLP). Additionally, this book discusses Transformers, the most recent advancement in NLP.
What you will learn
● Create machine learning models for classification and regression.
● Utilize TensorFlow 1.x to implement neural networks.
● Work with the Keras API and TensorFlow 2.
● Learn to design and train image categorization models.
● Construct translation and Q & A apps using transformer-based language models.
Who this book is for
This book is intended for those new to Deep Learning who want to learn about its principles and applications. Before you begin, you'll need to be familiar with Python.
Table of Contents
1. Introduction to Artificial Intelligence
2. Machine Learning
3. TensorFlow Programming
4. Neural Networks
5. TensorFlow 2
6. Image Recognition
7. Speech Recognition
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