Python Deep Learning, Vasilev I., 2023

Python Deep Learning, Vasilev I., 2023.

   The book will start from the theoretical foundations of deep neural networks (NN), and it will delve into the most popular network architectures – transformers, transformer-based large language models (LLMs), and convolutional networks. It will introduce these models in the context of various computer vision and natural language processing (NLP) examples, including state-of-the-art applications such as text-to-image generation and chatbots.




Deep Learning Fundamentals.
In this chapter, we will introduce deep learning (DL) and deep neural networks (DNNs) - that is, neural networks (NNs) with multiple hidden layers. You might be wondering what the point of using more than one hidden layer is, given the universal approximation theorem. This is in no way a naive question, and for a long time, NNs were used in that way.

Without going into too much detail, one reason is that approximating a complex function might require a huge number of units in the hidden layer, making it impractical to use. There is also another, more important, reason for using deep networks, which is not directly related to the number of hidden layers, but to the level of learning. A deep network does not simply learn to predict output Y given input, X; it also understands the basic features of the input. It’s able to learn abstractions of features of input samples, understand the basic characteristics of the samples, and make predictions based on those characteristics. This level of abstraction is missing in other basic machine learning (ML) algorithms and shallow NNs.

Content.
Preface.
Part 1: Introduction to Neural Networks.
1 Machine Learning – an Introduction.
2 Neural Networks.
3 Deep Learning Fundamentals.
Part 2: Deep Neural Networks for Computer Vision.
4 Computer Vision with Convolutional Networks.
5 Advanced Computer Vision Applications.
Part 3: Natural Language Processing and Transformers.
6 Natural Language Processing and Recurrent Neural Networks.
7 The Attention Mechanism and Transformers.
8 Exploring Large Language Models in Depth.
9 Advanced Applications of Large Language Models.
Part 4: Developing and Deploying Deep Neural Networks.
10 Machine Learning Operations (MLOps).
Index.
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