Python Deep Learning (eBook)

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.

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Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using PythonKey FeaturesUnderstand the theory, mathematical foundations and structure of deep neural networksBecome familiar with transformers, large language models, and convolutional networksLearn how to apply them to various computer vision and natural language processing problemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe 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.What you will learnEstablish theoretical foundations of deep neural networksUnderstand convolutional networks and apply them in computer vision applicationsBecome well versed with natural language processing and recurrent networksExplore the attention mechanism and transformersApply transformers and large language models for natural language and computer visionImplement coding examples with PyTorch, Keras, and Hugging Face TransformersUse MLOps to develop and deploy neural network modelsWho this book is forThis book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.]]>