Hands On Python And Pytorch
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Hands-On Python and PyTorch
Author: Sarful Hassan
language: en
Publisher: Independently Published
Release Date: 2025-02-04
Hands-On Python and PyTorch: A Practical Guide to Deep Learning Master Deep Learning with Python and PyTorch Are you ready to dive into the world of deep learning and AI? Hands-On Python and PyTorch: A Practical Guide to Deep Learning is your step-by-step companion to mastering neural networks, machine learning models, and real-world AI applications with Python and PyTorch. Why This Book? ✅ Comprehensive & Hands-On - Covers everything from basic PyTorch operations to advanced deep learning techniques. ✅ Real-World Applications - Learn to build image classifiers, NLP models, GANs, and reinforcement learning systems. ✅ AI & Deep Learning Integration - Understand how PyTorch works with TensorFlow, OpenCV, and other AI frameworks. ✅ Optimized for Python - Uses Python 3.x for efficient and scalable implementation. ✅ Beginner to Expert Guide - Suitable for students, developers, data scientists, and AI enthusiasts looking to master PyTorch and deep learning. What You'll Learn ✔️ Setting up PyTorch and Python for deep learning projects ✔️ Core PyTorch concepts: Tensors, Autograd, and Modules ✔️ Building and training neural networks from scratch ✔️ Advanced optimization techniques and model tuning ✔️ Real-time applications in computer vision, NLP, and reinforcement learning ✔️ Deploying AI models efficiently for production Who Should Read This Book?
PyTorch for Beginners
Author: Jason Brener
language: en
Publisher: Independently Published
Release Date: 2025-07-31
PyTorch for Beginners: A Hands-On Guide to Deep Learning with Python PyTorch for Beginners is a practical, beginner-friendly introduction to building deep learning models using Python and PyTorch. This book demystifies the world of neural networks by guiding readers through real-world projects and step-by-step implementations, all without requiring a background in machine learning or advanced mathematics. Whether you're just starting your journey in artificial intelligence or switching from another framework, this guide helps you gain a solid foundation and hands-on experience with one of today's most popular deep learning libraries. With a clear focus on practical applications, the book covers everything from tensors and automatic differentiation to building and training your first neural network. By the end, you'll be comfortable creating models for tasks like image classification, natural language processing, and more empowered to take on real-world deep learning challenges with confidence. This book simplifies deep learning by combining theoretical insights with code-driven learning. Using PyTorch, one of the most flexible and beginner-friendly frameworks, you'll learn to work with tensors, train models, and understand how neural networks operate under the hood. Each chapter builds on the last, offering progressively deeper insights into model design, optimization, and deployment. Key Features of This Book Step-by-step tutorials with fully documented PyTorch code Real-world projects covering vision and NLP applications Clear explanations of core deep learning concepts Best practices for training, debugging, and optimizing models Hands-on exercises to reinforce learning at each stage A complete companion code repository for experimentation This book is ideal for Python developers, students, data enthusiasts, and aspiring machine learning engineers who want to break into deep learning using a practical, project-based approach. No prior experience with PyTorch or deep learning is required just a willingness to learn and experiment. Whether you're building your first neural network or preparing for a deep learning role, PyTorch for Beginners is your gateway into modern AI development. Grab your copy now and start building real-world deep learning models with confidence one line of PyTorch code at a time!
Hands-On One-shot Learning with Python
Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key FeaturesLearn how you can speed up the deep learning process with one-shot learningUse Python and PyTorch to build state-of-the-art one-shot learning modelsExplore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learningBook Description One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models. What you will learnGet to grips with the fundamental concepts of one- and few-shot learningWork with different deep learning architectures for one-shot learningUnderstand when to use one-shot and transfer learning, respectivelyStudy the Bayesian network approach for one-shot learningImplement one-shot learning approaches based on metrics, models, and optimization in PyTorchDiscover different optimization algorithms that help to improve accuracy even with smaller volumes of dataExplore various one-shot learning architectures based on classification and regressionWho this book is for If you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.