Deep Learning With Fastai Cookbook

Author : Mark Ryan
Publisher : Packt Publishing Ltd
ISBN 13 : 1800209991
Total Pages : 340 pages
Book Rating : 4.92/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning with fastai Cookbook written by Mark Ryan and published by Packt Publishing Ltd. This book was released on 2021-09-24 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Harness the power of the easy-to-use, high-performance fastai framework to rapidly create complete deep learning solutions with few lines of code Key FeaturesDiscover how to apply state-of-the-art deep learning techniques to real-world problemsBuild and train neural networks using the power and flexibility of the fastai frameworkUse deep learning to tackle problems such as image classification and text classificationBook Description fastai is an easy-to-use deep learning framework built on top of PyTorch that lets you rapidly create complete deep learning solutions with as few as 10 lines of code. Both predominant low-level deep learning frameworks, TensorFlow and PyTorch, require a lot of code, even for straightforward applications. In contrast, fastai handles the messy details for you and lets you focus on applying deep learning to actually solve problems. The book begins by summarizing the value of fastai and showing you how to create a simple 'hello world' deep learning application with fastai. You'll then learn how to use fastai for all four application areas that the framework explicitly supports: tabular data, text data (NLP), recommender systems, and vision data. As you advance, you'll work through a series of practical examples that illustrate how to create real-world applications of each type. Next, you'll learn how to deploy fastai models, including creating a simple web application that predicts what object is depicted in an image. The book wraps up with an overview of the advanced features of fastai. By the end of this fastai book, you'll be able to create your own deep learning applications using fastai. You'll also have learned how to use fastai to prepare raw datasets, explore datasets, train deep learning models, and deploy trained models. What you will learnPrepare real-world raw datasets to train fastai deep learning modelsTrain fastai deep learning models using text and tabular dataCreate recommender systems with fastaiFind out how to assess whether fastai is a good fit for a given problemDeploy fastai deep learning models in web applicationsTrain fastai deep learning models for image classificationWho this book is for This book is for data scientists, machine learning developers, and deep learning enthusiasts looking to explore the fastai framework using a recipe-based approach. Working knowledge of the Python programming language and machine learning basics is strongly recommended to get the most out of this deep learning book.

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.96/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Author : Yuxi (Hayden) Liu
Publisher : Packt Publishing Ltd
ISBN 13 : 1838553231
Total Pages : 334 pages
Book Rating : 4.34/5 ( download)

DOWNLOAD NOW!

Download or read book PyTorch 1.x Reinforcement Learning Cookbook written by Yuxi (Hayden) Liu and published by Packt Publishing Ltd. This book was released on 2019-10-31 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipes Key FeaturesUse PyTorch 1.x to design and build self-learning artificial intelligence (AI) modelsImplement RL algorithms to solve control and optimization challenges faced by data scientists todayApply modern RL libraries to simulate a controlled environment for your projectsBook Description Reinforcement learning (RL) is a branch of machine learning that has gained popularity in recent times. It allows you to train AI models that learn from their own actions and optimize their behavior. PyTorch has also emerged as the preferred tool for training RL models because of its efficiency and ease of use. With this book, you'll explore the important RL concepts and the implementation of algorithms in PyTorch 1.x. The recipes in the book, along with real-world examples, will help you master various RL techniques, such as dynamic programming, Monte Carlo simulations, temporal difference, and Q-learning. You'll also gain insights into industry-specific applications of these techniques. Later chapters will guide you through solving problems such as the multi-armed bandit problem and the cartpole problem using the multi-armed bandit algorithm and function approximation. You'll also learn how to use Deep Q-Networks to complete Atari games, along with how to effectively implement policy gradients. Finally, you'll discover how RL techniques are applied to Blackjack, Gridworld environments, internet advertising, and the Flappy Bird game. By the end of this book, you'll have developed the skills you need to implement popular RL algorithms and use RL techniques to solve real-world problems. What you will learnUse Q-learning and the state–action–reward–state–action (SARSA) algorithm to solve various Gridworld problemsDevelop a multi-armed bandit algorithm to optimize display advertisingScale up learning and control processes using Deep Q-NetworksSimulate Markov Decision Processes, OpenAI Gym environments, and other common control problemsSelect and build RL models, evaluate their performance, and optimize and deploy themUse policy gradient methods to solve continuous RL problemsWho this book is for Machine learning engineers, data scientists and AI researchers looking for quick solutions to different reinforcement learning problems will find this book useful. Although prior knowledge of machine learning concepts is required, experience with PyTorch will be useful but not necessary.

Author : Bernhard J Mayr Mba
Publisher :
ISBN 13 :
Total Pages : 342 pages
Book Rating : 4.99/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning Examples with PyTorch and Fastai written by Bernhard J Mayr Mba and published by . This book was released on 2020-09-29 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: The concept of Deep Learning utilizes deep neural nets to accomplish task from artificial intelligence like: Computer Vision: Image Classification, Object Detection / Tracking Natural Language Understanding: Text Analyses, Language Translation, Image Caption Generation... ... The Book Deep Learning Examples with PyTorch and fastai - A Developers' Cookbook is full of practical examples on how to apply the deep learning frameworks PyTorch and fastai on different problems. What's inside the book? Build an Image Classifier from Scratch How does SGD - Stochastic Gradient Descent - work? Multi-Label Classification Cross-Fold-Validation FastAI - A Glance on the internal API of the deep learning framework Image Segmentation Style-Transfer Server deployment of deep learning models Keypoints Detection Object Detection Super-resolution GANs Siamese Twins Tabular Data with FastAI Ensembling Models with TabularData Analyzing Neural Nets with the SHAP Library Introduction to Natural Language Processing

Author : Douwe Osinga
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491995793
Total Pages : 252 pages
Book Rating : 4.92/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning Cookbook written by Douwe Osinga and published by "O'Reilly Media, Inc.". This book was released on 2018-06-05 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning doesn’t have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you’ll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You’ll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections

Author : Anirudh Koul
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492034819
Total Pages : 585 pages
Book Rating : 4.10/5 ( download)

DOWNLOAD NOW!

Download or read book Practical Deep Learning for Cloud, Mobile, and Edge written by Anirudh Koul and published by "O'Reilly Media, Inc.". This book was released on 2019-10-14 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users

Author : Kunal Sawarkar
Publisher : Packt Publishing Ltd
ISBN 13 : 1800569270
Total Pages : 366 pages
Book Rating : 4.70/5 ( download)

DOWNLOAD NOW!

Download or read book Deep Learning with PyTorch Lightning written by Kunal Sawarkar and published by Packt Publishing Ltd. This book was released on 2022-04-29 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper Key FeaturesBecome well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domainsSpeed up your research using PyTorch Lightning by creating new loss functions, networks, and architecturesTrain and build new algorithms for massive data using distributed trainingBook Description PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time. You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging. By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning. What you will learnCustomize models that are built for different datasets, model architectures, and optimizersUnderstand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be builtUse out-of-the-box model architectures and pre-trained models using transfer learningRun and tune DL models in a multi-GPU environment using mixed-mode precisionsExplore techniques for model scoring on massive workloadsDiscover troubleshooting techniques while debugging DL modelsWho this book is for This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.

Author : Chris Albon
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491989335
Total Pages : 305 pages
Book Rating : 4.33/5 ( download)

DOWNLOAD NOW!

Download or read book Machine Learning with Python Cookbook written by Chris Albon and published by "O'Reilly Media, Inc.". This book was released on 2018-03-09 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models

Author : Lewis Tunstall
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098136764
Total Pages : 409 pages
Book Rating : 4.65/5 ( download)

DOWNLOAD NOW!

Download or read book Natural Language Processing with Transformers, Revised Edition written by Lewis Tunstall and published by "O'Reilly Media, Inc.". This book was released on 2022-05-26 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Author : Bharath Ramsundar
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491980400
Total Pages : 256 pages
Book Rating : 4.08/5 ( download)

DOWNLOAD NOW!

Download or read book TensorFlow for Deep Learning written by Bharath Ramsundar and published by "O'Reilly Media, Inc.". This book was released on 2018-03-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units