000			03259cam a2200289zu 4500
001			88909815
003			FRCYB88909815
005			20251020123909.0
006			m o d
007			cr un
008			251020s2021 fr | o|||||0|0|||eng d
020			_a9781789614381
035			_aFRCYB88909815
040			_aFR-PaCSA _ben _c _erda
100	1		_aJha, Ashish Ranjan
245	0	1	_aMastering PyTorch _c['Jha, Ashish Ranjan', 'Pillai, Dr. Gopinath']
264		1	_bPackt Publishing _c2021
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aJha, Ashish Ranjan
700	0		_aPillai, Dr. Gopinath
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88909815 _qtext/html _a
520			_aMaster advanced techniques and algorithms for deep learning with PyTorch using real-world examplesKey FeaturesUnderstand how to use PyTorch 1.x to build advanced neural network modelsLearn to perform a wide range of tasks by implementing deep learning algorithms and techniquesGain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook DescriptionDeep learning is driving the AI revolution, and 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 out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures 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 and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. 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.What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
999			_c1555697 _d1555697