Deep Learning Examples With Pytorch And Fastai
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Deep Learning Examples With Pytorch And Fastai
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Author : Bernhard J Mayr Mba
language : en
Publisher:
Release Date : 2020-09-29
Deep Learning Examples With Pytorch And Fastai written by Bernhard J Mayr Mba and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-29 with categories.
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
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
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
Deep Learning With Fastai Cookbook
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Author : Mark Ryan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-24
Deep Learning With Fastai Cookbook written by Mark Ryan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-24 with Computers categories.
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.
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
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
Deep Learning With Fast Ai
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-01
Deep Learning With Fast Ai written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-01 with Computers categories.
"Deep Learning with Fast.ai" "Deep Learning with Fast.ai" provides a comprehensive and contemporary roadmap for mastering deep learning through the lens of the Fast.ai ecosystem. The book opens by expertly blending the history, principles, and philosophy of modern neural networks with Fast.ai's distinctive top-down, practical teaching methodology, and design. Readers are introduced to the powerful abstractions and extensibility of Fast.ai, which leverages PyTorch for a seamless, high-performance user experience. Through clear explanations of core concepts—ranging from reproducibility and responsible AI to balancing mathematical theory with hands-on application—the book sets a strong foundation for learners and professionals alike. The book delves deeply into real-world workflows, guiding practitioners through flexible data pipelines, rigorous data augmentation, and innovative semi-supervised and out-of-core processing, all while addressing the challenges of diverse data sources. Subsequent chapters thoroughly unpack modeling fundamentals, from the versatile Learner abstractions and event-driven callbacks to advanced optimization, regularization, and efficient resource management. Covering transfer learning, model fine-tuning, and domain adaptation, the text empowers users to adapt state-of-the-art techniques for both typical and novel scenarios across computer vision, natural language processing, and tabular data—supplemented by practical chapters on model deployment, interpretation, and monitoring in production environments. Designed with the needs of modern machine learning practitioners and researchers in mind, "Deep Learning with Fast.ai" goes beyond standard use cases to explore innovative avenues such as integrating with external libraries, implementing custom neural components, and scaling for industrial hardware. With dedicated discussions on security, adversarial robustness, ethics, explainability, and the evolving future of AI, this book serves as both a practical toolkit and a forward-looking reference. Whether you are a developer, data scientist, researcher, or educator, this volume invites you to unlock the potential of deep learning with clarity, responsibility, and cutting-edge best practices.
Machine Learning For Tabular Data
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Author : Mark Ryan
language : en
Publisher: Simon and Schuster
Release Date : 2025-03-04
Machine Learning For Tabular Data written by Mark Ryan and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-04 with Computers categories.
Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Machine Learning for Tabular Data teaches you to train insightful machine learning models on common tabular business data sources such as spreadsheets, databases, and logs. You’ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline. Machine Learning for Tabular Data will teach you how to: • Pick the right machine learning approach for your data • Apply deep learning to tabular data • Deploy tabular machine learning locally and in the cloud • Pipelines to automatically train and maintain a model Machine Learning for Tabular Data covers classic machine learning techniques like gradient boosting, and more contemporary deep learning approaches. By the time you’re finished, you’ll be equipped with the skills to apply machine learning to the kinds of data you work with every day. Foreword by Antonio Gulli. About the technology Machine learning can accelerate everyday business chores like account reconciliation, demand forecasting, and customer service automation—not to mention more exotic challenges like fraud detection, predictive maintenance, and personalized marketing. This book shows you how to unlock the vital information stored in spreadsheets, ledgers, databases and other tabular data sources using gradient boosting, deep learning, and generative AI. About the book Machine Learning for Tabular Data delivers practical ML techniques to upgrade every stage of the business data analysis pipeline. In it, you’ll explore examples like using XGBoost and Keras to predict short-term rental prices, deploying a local ML model with Python and Flask, and streamlining workflows using large language models (LLMs). Along the way, you’ll learn to make your models both more powerful and more explainable. What's inside • Master XGBoost • Apply deep learning to tabular data • Deploy models locally and in the cloud • Build pipelines to train and maintain models About the reader For readers experienced with Python and the basics of machine learning. About the author Mark Ryan is the AI Lead of the Developer Knowledge Platform at Google. A three-time Kaggle Grandmaster, Luca Massaron is a Google Developer Expert (GDE) in machine learning and AI. He has published 17 other books. Table of Contents Part 1 1 Understanding tabular data 2 Exploring tabular datasets 3 Machine learning vs. deep learning Part 2 4 Classical algorithms for tabular data 5 Decision trees and gradient boosting 6 Advanced feature processing methods 7 An end-to-end example using XGBoost Part 3 8 Getting started with deep learning with tabular data 9 Deep learning best practices 10 Model deployment 11 Building a machine learning pipeline 12 Blending gradient boosting and deep learning A Hyperparameters for classical machine learning models B K-nearest neighbors and support vector machines
Programming Pytorch For Deep Learning
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Author : Ian Pointer
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-09-20
Programming Pytorch For Deep Learning written by Ian Pointer and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-20 with Computers categories.
Take the next steps toward mastering deep learning, the machine learning method that’s transforming the world around us by the second. In this practical book, you’ll get up to speed on key ideas using Facebook’s open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text,and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production. Learn how to deploy deep learning models to production Explore PyTorch use cases from several leading companies Learn how to apply transfer learning to images Apply cutting-edge NLP techniques using a model trained on Wikipedia Use PyTorch’s torchaudio library to classify audio data with a convolutional-based model Debug PyTorch models using TensorBoard and flame graphs Deploy PyTorch applications in production in Docker containers and Kubernetes clusters running on Google Cloud
Mathematical Engineering Of Deep Learning
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Author : Benoit Liquet
language : en
Publisher: CRC Press
Release Date : 2024-10-03
Mathematical Engineering Of Deep Learning written by Benoit Liquet and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-03 with Computers categories.
Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning. Key Features: A perfect summary of deep learning not tied to any computer language, or computational framework. An ideal handbook of deep learning for readers that feel comfortable with mathematical notation. An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains. The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials. Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.
Pytorch Pocket Reference
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Author : Joe Papa
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-05-11
Pytorch Pocket Reference written by Joe Papa and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Computers categories.
This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem
Pytorch Cookbook
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Author : Matthew Rosch
language : en
Publisher: GitforGits
Release Date : 2023-10-04
Pytorch Cookbook written by Matthew Rosch and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-04 with Computers categories.
Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters. The book simplifies neural networks, training, optimization, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasizes distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them. This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning. Key Learnings Comprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning. Practical demonstrations of various neural networks, enhancing understanding through hands-on practice. Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields. In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions. Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects. Real-world application insights, bridging the gap between theoretical knowledge and practical execution. Focus on mobile and embedded development with PyTorch, leading to on-device AI. Emphasis on error handling and troubleshooting, preparing readers for real-world challenges. Advanced topics like real-time inferencing and model compression, providing future ready skill. Table of Content Introduction to PyTorch 2.0 Deep Learning Building Blocks Convolutional Neural Networks Recurrent Neural Networks Natural Language Processing Graph Neural Networks (GNNs) Working with Popular PyTorch Tools Distributed Training and Scalability Mobile and Embedded Development