Book Image

Python Deep Learning - Third Edition

By : Ivan Vasilev
4 (1)
Book Image

Python Deep Learning - Third Edition

4 (1)
By: Ivan Vasilev

Overview of this book

The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
Table of Contents (17 chapters)
1
Part 1:Introduction to Neural Networks
5
Part 2: Deep Neural Networks for Computer Vision
8
Part 3: Natural Language Processing and Transformers
13
Part 4: Developing and Deploying Deep Neural Networks

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Use opencv-python to read the RGB image located at image_file_path.”

A block of code is set as follows:

def build_fe_model():
    """"Create feature extraction model from the pre-trained model ResNet50V2"""
    # create the pre-trained part of the network, excluding FC layers
    base_model = tf.keras.applications.MobileNetV3Small(

Any command-line input or output is written as follows:

import tensorflow.keras

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import io
image = Image.open(io.BytesIO(response.content)) 
image.show()

Tips or important notes

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