Mastering PyTorch - Second Edition: Build powerful deep learning architectures using advanced PyTorch features. Ashish Ranjan Jha
Mastering-PyTorch-Second-Edition.pdf
ISBN: 9781801074308 | 538 pages | 14 Mb
- Mastering PyTorch - Second Edition: Build powerful deep learning architectures using advanced PyTorch features
- Ashish Ranjan Jha
- Page: 538
- Format: pdf, ePub, fb2, mobi
- ISBN: 9781801074308
- Publisher: Packt Publishing
Free online books to download on iphone Mastering PyTorch - Second Edition: Build powerful deep learning architectures using advanced PyTorch features
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Understand how to use PyTorch to build advanced neural network models including graph neural networks and reinforcement learning models Learn the latest tech, such as generating images from text using diffusion models Become an expert in deploying PyTorch models in the cloud, on mobile and across platforms Get the best from PyTorch by working with key libraries, including Hugging Face, fast.ai, and PyTorch Lightning PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most from your data and build complex neural network models. You'll create convolutional neural networks (CNNs) for image classification and recurrent neural networks (RNNs) and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production, including mobiles and embedded devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fast.ai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI, create recommendation systems using TorchRec, and build language and vision transformers with Hugging Face. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. Implement text, image, and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobiles and embedded devices Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Develop your own recommendation system using TorchRec Design ResNets, LSTMs, and graph neural networks Create language and vision transformer models using Hugging Face This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is an ideal resource for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python programming is required. Overview of Deep Learning with PyTorch Combining CNNs and LSTMs Deep CNN architectures Deep Recurrent Model Architectures Hybrid Advanced Neural Networks Graph Neural Networks Music and Text Generation with LSTMs Neural Style Transfer Image to Text Generation (Imagen/DALL-E) Deep Reinforcement Learning Model Training Optimisations Operationalizing PyTorch Models into Production PyTorch on Mobile and Embedded Devices Rapid Prototyping with PyTorch PyTorch and AutoML PyTorch and ExplainableAI Recommendation systems with TorchRec PyTorch x HuggingFace
Mastering PyTorch: Build powerful deep learning
Mastering PyTorch: Build powerful deep learning architectures using advanced PyTorch features | Jha, Ashish Ranjan | ISBN: 9781801074308 | Kostenloser
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Summary | Mastering PyTorch - Second Edition
In this chapter, we applied the concept of generative machine learning to images by generating an image that contains the content of one image and the style