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Pytorch Deep Learning by Example, Vol. 2

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Pytorch Deep Learning by Example Vol. 1

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PyTorch Deep Learning Hands-On

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch Key Features Internals and principles of PyTorch;

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Mastering PyTorch

PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and;

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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 PyTorch Quick Start Guide

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases;

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PyTorch Recipes

code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as;

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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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Programming PyTorch for Deep Learning Creating and Deploying Deep Learning Applications

Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second;

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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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Deep Learning with PyTorch

deep learning libraries-PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its;

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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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Hands-On Natural Language Processing with PyTorch 1.x

insights from text by building deep learning models for natural language processing (NLP) tasks. Starting by understanding how to install PyTorch;

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Advanced Deep Learning for Engineers and Scientists

effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and Python;

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Beginning Anomaly Detection Using Python-Based Deep Learning

and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly;

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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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Deep Learning for Coders with fastai &

in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web;

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Deep Reinforcement Learning with Python

foundation of deep reinforcement learning. Next, you'll study model-free learning followed by function approximation using neural networks and;

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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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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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Computer Vision Projects with PyTorch

will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn Solve problems;

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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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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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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 in Computational Mechanics

bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning;

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Deep Learning in Computational Mechanics

bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning;

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