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Deep learning with fastai cookbook leverage the easy-to-use fastai framework to unlock the power of deep learning /
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 Features Discover how to apply state-of-the-art deep learning techniques to real-world problems Build and train neural networks using the power and fl...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 245 | 1 | 0 | |a Deep learning with fastai cookbook |b leverage the easy-to-use fastai framework to unlock the power of deep learning / |c Mark Ryan. |
| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2021. | |
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| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started with fastai -- Technical requirements -- Setting up a fastai environment in Paperspace Gradient -- Getting ready -- How to do it... -- How it works... -- There's more... -- Setting up a fastai environment in Google Colab -- Getting ready -- How to do it... -- How it works... -- There's more... -- Setting up JupyterLab environment in Gradient -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world"" for fastai -- creating a model for MNIST -- Getting ready... | |
| 505 | 8 | |a How to do it... -- How it works... -- There's more... -- Understanding the world in four applications: tables, text, recommender systems, and images -- Getting ready -- How to do it... -- How it works... -- Working with PyTorch tensors -- Getting ready -- How to do it... -- How it works... -- There's more... -- Contrasting fastai with Keras -- Getting ready -- How to do it... -- How it works... -- Test your knowledge -- Chapter 2: Exploring and Cleaning Up Data with fastai -- Technical requirements -- Getting the complete set of oven-ready fastai datasets -- Getting ready -- How to do it... -- How it works... | |
| 505 | 8 | |a There's more... -- Examining tabular datasets with fastai -- Getting ready -- How to do it... -- How it works... -- There's more... -- Examining text datasets with fastai -- Getting ready -- How to do it... -- How it works... -- Examining image datasets with fastai -- Getting ready -- How to do it... -- How it works... -- There's more... -- Cleaning up raw datasets with fastai -- Getting ready -- How to do it... -- How it works... -- Chapter 3: Training Models with Tabular Data -- Technical requirements -- Training a model in fastai with a curated tabular dataset -- Getting ready -- How to do it... -- How it works... | |
| 505 | 8 | |a Training a model in fastai with a non-curated tabular dataset -- Getting ready -- How to do it... -- How it works... -- Training a model with a standalone dataset -- Getting ready -- How to do it... -- How it works... -- Assessing whether a tabular dataset is a good candidate for fastai -- Getting ready -- How to do it... -- How it works... -- Saving a trained tabular model -- Getting ready -- How to do it... -- How it works... -- Test your knowledge -- Getting ready -- Chapter 4: Training Models with Text Data -- Technical requirements -- Training a deep learning language model with a curated IMDb text dataset | |
| 505 | 8 | |a Getting ready -- How to do it... -- How it works... -- There's more... -- Training a deep learning classification model with a curated text dataset -- Getting ready -- How to do it... -- How it works... -- There's more... -- Training a deep learning language model with a standalone text dataset -- Getting ready -- How to do it... -- How it works... -- Training a deep learning text classifier with a standalone text dataset -- Getting ready -- How to do it... -- How it works... -- Test your knowledge -- Getting ready -- How to do it... -- Chapter 5: Training Recommender Systems -- Technical requirements | |
| 520 | |a 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 Features Discover how to apply state-of-the-art deep learning techniques to real-world problems Build and train neural networks using the power and flexibility of the fastai framework Use deep learning to tackle problems such as image classification and text classification Book 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 learn Prepare real-world raw datasets to train fastai deep learning models Train fastai deep learning models using text and tabular data Create recommender systems with fastai Find out how to assess whether fastai is a good fit for a given problem Deploy fastai deep learning models in web applications Train fastai deep learning models for image classification Who 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 de ... | ||
| 542 | |f Copyright © 2021 Packt Publishing |g 2021 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence. |2 fast |0 (OCoLC)fst00817247 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 650 | 2 | |a Artificial Intelligence |0 (DNLM)D001185 | |
| 650 | 2 | |a Neural Networks, Computer |0 (DNLM)D016571 | |
| 776 | 0 | 8 | |i Print version : |z 9781800208100 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781800208100/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 938 | |a Askews and Holts Library Services |b ASKH |n AH38807480 | ||
| 994 | |a 92 |b IZTAP | ||