Become well-versed with the theory of deep neural networks, including convolutional, recurrent, graph, and the new transformer architectures. Use Python to apply these models to various computer vision and natural language processing tasks.
Key Features
- Understand the building blocks and mathematical foundations of neural networks
- Become familiar with convolutional, recurrent, graph, and transformers networks
- Learn how to apply them on various computer vision and natural language processing tasks
Book Description
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.
This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.
By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
What you will learn
- Building blocks, structure, and theoretical foundations of neural networks
- Convolutional networks theory and different convolutional architectures
- Learn to solve image classification, object detection, and image segmentation
- Recurrent neural networks theory and implementation
- Language modelling, text classification, question answering, text generation
- The attention mechanism and transformers, including large language models
- Graph neural networks and their applications
- Implement various tasks with PyTorch and Keras
- Deploy neural network models in production environments
Who This Book Is For
This book is for people already familiar with programming: software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning who have a Python programming experience.
Table of Contents
- Machine Learning – An Introduction
- Neural Networks
- Deep Learning Fundamentals
- Computer Vision With Convolutional Networks
- Advanced Computer Vision Tasks
- Recurrent Neural Networks and Language Models
- The Attention Mechanism and Transformers
- Applications of Transformers
- Graph Neural Networks
- Machine Learning Operations (ML Ops)
Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, with whom he continued its development. He has also worked as a machine learning engineer and researcher in medical image classification and segmentation with deep neural networks. Since 2017, he has focused on financial machine learning. He co-founded an algorithmic trading company, where he's the lead engineer. He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic.
