Video description
In Video Editions the narrator reads the book while the content, figures, code listings, diagrams, and text appear on the screen. Like an audiobook that you can also watch as a video.
Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.
In Inside Deep Learning, you will learn how to:
- Implement deep learning with PyTorch
- Select the right deep learning components
- Train and evaluate a deep learning model
- Fine tune deep learning models to maximize performance
- Understand deep learning terminology
- Adapt existing PyTorch code to solve new problems
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped—you’ll dive into math, theory, and practical applications. Everything is clearly explained in plain English.
About the Technology
Deep learning doesn’t have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don’t have to be a mathematics expert or a senior data scientist to grasp what’s going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence.
About the Book
Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You’ll explore deep learning concepts and tools through plain language explanations, annotated code, and dozens of instantly useful PyTorch examples. Each type of neural network is clearly presented without complex math, and every solution in this book can run using readily available GPU hardware!
What's Inside
- Select the right deep learning components
- Train and evaluate a deep learning model
- Fine tune deep learning models to maximize performance
- Understand deep learning terminology
About the Reader
For Python programmers with basic machine learning skills.
About the Author
Edward Raff is a Chief Scientist at Booz Allen Hamilton, and the author of the JSAT machine learning library.
Quotes
Pick up this book, and you won’t be able to put it down. A rich, engaging knowledge base of deep learning math, algorithms, and models—just like the title says!
- From the Foreword by Kirk Borne Ph.D., Chief Science Officer, DataPrime.ai
The clearest and easiest book for learning deep learning principles and techniques I have ever read. The graphical representations for the algorithms are an eye-opening revelation.
- Richard Vaughan, Purple Monkey Collective
A great read for anyone interested in understanding the details of deep learning.
- Vishwesh Ravi Shrimali, MBRDI
Table of contents
- Part 1. Foundational methods
- Chapter 1. The mechanics of learning
- Chapter 1. The world as tensors
- Chapter 1. Automatic differentiation
- Chapter 1. Optimizing parameters
- Chapter 1. Loading dataset objects
- Chapter 1. Summary
- Chapter 2. Fully connected networks
- Chapter 2. Building our first neural network
- Chapter 2. Classification problems
- Chapter 2. Better training code
- Chapter 2. Training in batches
- Chapter 2. Summary
- Chapter 3. Convolutional neural networks
- Chapter 3. What are convolutions?
- Chapter 3. How convolutions benefit image processing
- Chapter 3. Putting it into practice: Our first CNN
- Chapter 3. Adding pooling to mitigate object movement
- Chapter 3. Data augmentation
- Chapter 3. Summary
- Chapter 4. Recurrent neural networks
- Chapter 4. RNNs in PyTorch
- Chapter 4. Improving training time with packing
- Chapter 4. More complex RNNs
- Chapter 4. Summary
- Chapter 5. Modern training techniques
- Chapter 5. Learning rate schedules
- Chapter 5. Making better use of gradients
- Chapter 5. Hyperparameter optimization with Optuna
- Chapter 5. Summary
- Chapter 6. Common design building blocks
- Chapter 6. Normalization layers: Magically better convergence
- Chapter 6. Skip connections: A network design pattern
- Chapter 6. 1 × 1 Convolutions: Sharing and reshaping information in channels
- Chapter 6. Residual connections
- Chapter 6. Long short-term memory RNNs
- Chapter 6. Summary
- Part 2. Building advanced networks
- Chapter 7. Autoencoding and self-supervision
- Chapter 7. Designing autoencoding neural networks
- Chapter 7. Bigger autoencoders
- Chapter 7. Denoising autoencoders
- Chapter 7. Autoregressive models for time series and sequences
- Chapter 7. Summary
- Chapter 8. Object detection
- Chapter 8. Transposed convolutions for expanding image size
- Chapter 8. U-Net: Looking at fine and coarse details
- Chapter 8. Object detection with bounding boxes
- Chapter 8. Using the pretrained Faster R-CNN
- Chapter 8. Summary
- Chapter 9. Generative adversarial networks
- Chapter 9. Mode collapse
- Chapter 9. Wasserstein GAN: Mitigating mode collapse
- Chapter 9. Convolutional GAN
- Chapter 9. Conditional GAN
- Chapter 9. Walking the latent space of GANs
- Chapter 9. Ethics in deep learning
- Chapter 9. Summary
- Chapter 10. Attention mechanisms
- Chapter 10. Adding some context
- Chapter 10. Putting it all together: A complete attention mechanism with context
- Chapter 10. Summary
- Chapter 11. Sequence-to-sequence
- Chapter 11. Machine translation and the data loader
- Chapter 11. Inputs to Seq2Seq
- Chapter 11. Seq2Seq with attention
- Chapter 11. Summary
- Chapter 12. Network design alternatives to RNNs
- Chapter 12. Averaging embeddings over time
- Chapter 12. Pooling over time and 1D CNNs
- Chapter 12. Positional embeddings add sequence information to any model
- Chapter 12. Transformers: Big models for big data
- Chapter 12. Summary
- Chapter 13. Transfer learning
- Chapter 13. Transfer learning and training with CNNs
- Chapter 13. Learning with fewer labels
- Chapter 13. Pretraining with text
- Chapter 13. Summary
- Chapter 14. Advanced building blocks
- Chapter 14. Improved residual blocks
- Chapter 14. MixUp training reduces overfitting
- Chapter 14. Summary
- Appendix. Setting up Colab
Product information
- Title: Inside Deep Learning, Video Edition
- Author(s):
- Release date: June 2022
- Publisher(s): Manning Publications
- ISBN: None
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