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Deep Learning with PyTorch Quick Start Guide

those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and;

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PyTorch Deep Learning Hands-On

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;

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Mastering PyTorch

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use;

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The Deep Learning with PyTorch Workshop

Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key Features Learn how to define your own;

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PyTorch Recipes

deep learning models with fewer and smarter implementations Explore the PyTorch framework for model explainability and to bring transparency;

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Stevens:Deep Learning with PyTorc_p

started quickly and then grows with you as you, and your deep learning skills, become more sophisticated. Deep Learning with PyTorch;

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Deep Learning with Python

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;

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Deep Learning with Python

Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory;

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Advanced Deep Learning for Engineers and Scientists

applications. This book introduces a broad range of topics in deep learning. The authors start with the fundamentals, architectures, tools needed for;

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Deep Learning with PyTorch

, you'll be able to implement deep learning applications in PyTorch with ease. What you will learn Use PyTorch for GPU-accelerated tensor;

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PyTorch Recipes

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch;

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Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications

the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep;

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Hands-On Natural Language Processing with PyTorch 1.x

be solved using deep learning with PyTorch, but also be able to build models to solve them. What you will learn Use NLP techniques for;

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Beginning Anomaly Detection Using Python-Based Deep Learning

deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications;

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Deep Learning for Coders with fastai &

, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal;

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Natural Language Processing with PyTorchlow

how to apply these methods using PyTorch, a Python-based deep learning library. Authors Delip Rao and Brian McMahon provide you with a;

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Natural Language Processing with Python Quick Start Guide

, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven;

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Pytorch Deep Learning by Example Vol. 1

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Pytorch Deep Learning by Example, Vol. 2

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Computer Vision Projects with PyTorch

in computer vision with PyTorch. Implement transfer learning and perform image classification, object detection, image segmentation, and other;

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Hands-On One-shot Learning with Python

Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key Features Learn how you can speed up the deep;

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PyTorch 1.x Reinforcement Learning Cookbook

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key Features Use PyTorch 1.x to;

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PyTorch Pocket Reference

spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch;

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Hands-on Machine Learning with Python

learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;

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Deep Reinforcement Learning with Python

understand deep reinforcement learning along with deep q networks and policy gradient models implementation with TensorFlow, PyTorch, and Open AI Gym;

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Hands-on Machine Learning with Python

learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions;

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Deep Learning from Scratch Building with Python from First Principles

. You'll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch;

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