Getting Started With Deep Learning
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Getting Started with Deep Learning
Author: Ricardo Calix
language: en
Publisher: Createspace Independent Publishing Platform
Release Date: 2017-01-18
Ever since 2007 with the explosion in the use of parallel hardware, the field of machine learning has become more exciting and more promising. It seems that the dream of true AI is finally just around the corner. Certainly, there are many companies that are starting to rely heavily on AI for their products. These include companies in search like Facebook, Google, as well as retailers and multimedia companies like Amazon and Netflix. But more recently many others in the health-care and cyber security industries are also interested in what AI and machine learning can do for them. Some of these technologies such as Tensorflow (which came about around 2015) are new and not widely understood. In this book I hope to provide basic discussions of machine learning and in particular deep learning to help readers to quickly get started in using these technologies. The book is not a comprehensive survey on deep learning. There are many topics I do not cover here as too much material can be overwhelming to the un-initiated. There are many good books that cover all the theory in depth and I will mention some of them in the book. Instead, the goal in this book is to help people new to deep learning to quickly get started with these concepts using python and Tensorflow. Therefore, a lot of detail is spent on helping the reader to write his or her first deep network classifier. Additionally, I will try to connect several elements in machine learning which I think are related and are very important for data analysis and automatic classification. In general, I prefer python and I will try to present all examples using this great language. I will also use the more common libraries and the Linux development environment. Many people use SKlearn and I have therefore tried to use this library in the Tensorflow examples so that the focus is mainly on creating the deep layer network architectures.
Deep Learning with PyTorch
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2023-03-21
Deep learning is currently the most interesting and powerful machine learning technique. PyTorch is one of the dominant libraries for deep learning in the Python ecosystem and is widely used in research. With PyTorch, you can easily tap into the power of deep learning with just a few lines of code. Many deep learning models are created in PyTorch. Therefore, knowing PyTorch opens the door for you to leverage the power of deep learning. This Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects.
Mastering PyTorch
Author: Ashish Ranjan Jha
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-05-31
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks 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, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile 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 fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.