Hands On Deep Learning With Pytorch
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PyTorch Deep Learning Hands-On
Author: Sherin Thomas
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-04-30
Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key FeaturesInternals and principles of PyTorchImplement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook Description PyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly. PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools. Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch. This book is ideal if you want to rapidly add PyTorch to your deep learning toolset. What you will learn Use PyTorch to build: Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and moreConvolutional Neural Networks – create advanced computer vision systemsRecurrent Neural Networks – work with sequential data such as natural language and audioGenerative Adversarial Networks – create new content with models including SimpleGAN and CycleGANReinforcement Learning – develop systems that can solve complex problems such as driving or game playingDeep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packagesProduction-ready models – package your models for high-performance production environmentsWho this book is for Machine learning engineers who want to put PyTorch to work.
Hands-On Deep Learning with PyTorch
Author: CAMILA. JONES
language: en
Publisher: Independently Published
Release Date: 2025-01-26
Unlock the full potential of deep learning with this comprehensive, hands-on guide to PyTorch! Whether you are a beginner eager to learn the foundations of deep learning or an experienced practitioner looking to tackle advanced projects, this book has everything you need to get started and master PyTorch. Key Features: Practical, hands-on approach: Dive into real-world projects, step-by-step tutorials, and interactive code examples designed to teach you how to apply deep learning techniques effectively. Comprehensive coverage: From understanding the basics of neural networks and backpropagation to mastering advanced topics like CNNs, RNNs, transformers, GANs, and reinforcement learning. Build, train, and deploy models: Learn how to design and train cutting-edge models for a variety of applications, including image classification, natural language processing, and generative modeling. Real-world case studies: Explore practical use cases from industries like healthcare, finance, and autonomous vehicles, demonstrating how deep learning is transforming modern technology. Master PyTorch: Understand the core PyTorch concepts, including tensors, autograd, and GPU acceleration, and apply them in building efficient, scalable deep learning models. This book will take you through the complete deep learning workflow: Start with the basics: Learn the foundations of neural networks and PyTorch, setting up your development environment and writing your first deep learning model. Explore advanced techniques: Dive deep into convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) and LSTMs for time-series and NLP tasks, and the powerful transformer models that power state-of-the-art natural language processing. Hands-on projects: Build practical applications, from image classification and sentiment analysis to creating your own generative models like GANs for art and data generation. Deploy your models: Learn how to take your trained models and deploy them to production with tools like PyTorch Serve and ONNX, or convert them for use with other frameworks such as TensorFlow. Why This Book? With clear, simple explanations and practical code examples, this book simplifies complex deep learning concepts and turns theory into action. Each chapter includes exercises and challenges that reinforce your learning, making this a perfect resource for students, professionals, and anyone passionate about AI and machine learning. By the end of this book, you'll have the skills to: Build deep learning models using PyTorch for a variety of tasks. Fine-tune pre-trained models for your own applications. Work with the latest deep learning techniques and frameworks. Apply deep learning solutions in real-world scenarios. Perfect for: Beginners wanting to learn deep learning and PyTorch from scratch. Intermediate learners looking to advance their knowledge and tackle more complex models. Developers and AI enthusiasts who want to integrate deep learning into their projects. Get ready to build powerful, production-ready deep learning models. Start your journey with PyTorch today, and take the first step toward mastering AI!
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios. The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll Learn Review data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithm Understand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networks Get acquainted with scikit-learn and PyTorch Predict sequences in recurrent neural networks and long short term memory Who This Book Is For Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.