DOWNLOAD FREE Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD by Jeremy Howard, Sylvain Gugger Length: 624 pages Edition: 1 Language: English Publisher: O'Reilly Media Publication Date: 2020-08-04 ISBN-10: 1492045527 ISBN-13: 9781492045526 4.8 223 ratings Print Book Look Inside Description 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 Table of Contents I. Deep Learning in Practice 1. Your Deep Learning Journey 2. From Model to Production 3. Data Ethics II. Understanding fastai’s Applications 4. Under the Hood: Training a Digit Classifier 5. Image Classification 6. Other Computer Vision Problems 7. Training a State-of-the-Art Model 8. Collaborative Filtering Deep Dive 9. Tabular Modeling Deep Dive 10. NLP Deep Dive: RNNs 11. Data Munging with fastai’s Mid-Level API III. Foundations of Deep Learning 12. A Language Model from Scratch 13. Convolutional Neural Networks 14. ResNets 15. Application Architectures Deep Dive 16. The Training Process IV. Deep Learning from Scratch 17. A Neural Net from the Foundations 18. CNN Interpretation with CAM 19. A fastai Learner from Scratch 20. Concluding Thoughts A. Creating a Blog B. Data Project Checklist DOWNLOAD FREE

 

DOWNLOAD FREE

Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD

by Jeremy Howard, Sylvain Gugger

• Length: 624 pages
• Edition: 1
• Language: English
• Publisher: O'Reilly Media
• Publication Date: 2020-08-04
• ISBN-10: 1492045527
• ISBN-13: 9781492045526

4.8

223 ratings

 Print Book  Look Inside

Description

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

Table of Contents

I. Deep Learning in Practice
1. Your Deep Learning Journey
2. From Model to Production
3. Data Ethics

II. Understanding fastai’s Applications
4. Under the Hood: Training a Digit Classifier
5. Image Classification
6. Other Computer Vision Problems
7. Training a State-of-the-Art Model
8. Collaborative Filtering Deep Dive
9. Tabular Modeling Deep Dive
10. NLP Deep Dive: RNNs
11. Data Munging with fastai’s Mid-Level API

III. Foundations of Deep Learning
12. A Language Model from Scratch
13. Convolutional Neural Networks
14. ResNets
15. Application Architectures Deep Dive
16. The Training Process

IV. Deep Learning from Scratch
17. A Neural Net from the Foundations
18. CNN Interpretation with CAM
19. A fastai Learner from Scratch
20. Concluding Thoughts

A. Creating a Blog
B. Data Project Checklist

DOWNLOAD FREE