Deep Learning with PyTorch Step-by-Step (3-VOLUME BUNDLE)

THIS BUNDLE IS AVAILABLE AT MY NEW PAGE:

https://danielgodoy.gumroad.com/l/pytorch_bundle

GET THREE, PAY TWO - SAVE $15!

Why these books?

Are you looking for books where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code?

Technical books that are also easy and enjoyable to read?

This is it!

Are these books for me?

I wrote these books for beginners in general - not only PyTorch beginners.

Every now and then I will spend some time explaining some fundamental concepts which I believe are key to have a proper understanding of what's going on in the code.

• If you have absolutely no experience with PyTorch, the first volume is your starting point.
• If your goal is to learn about deep learning models for computer vision, and you’re already comfortable training simple models in PyTorch, the second volume is the right one for you.
• The third volume is more demanding than the other two, and you’re going to enjoy it more if you already have a solid understanding of deep learning models.

What will I learn?

Volume I

In the first volume of the series, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more.

By the time you finish this volume, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.

Volume II

In the second volume of the series, you’ll be introduced to deeper models and activation functions, convolutional neural networks, initialization schemes, learning rate schedulers, transfer learning, and more.

Volume III

In the third volume of the series, you’ll be introduced to all things sequence-related: recurrent neural networks and their variations, sequence-to-sequence models, attention, self-attention, and Transformers.

This volume also includes a crash course on natural language processing (NLP), from the basics of word tokenization all the way up to fine-tuning large models (BERT and GPT-2) using the HuggingFace library.

How are these books different?

• I wrote these books as if I were having a conversation with YOU, the reader: I will ask you questions (and give you answers shortly afterward) and I will also make (silly) jokes.
• They spell concepts out in plain English, avoiding fancy mathematical notation as much as possible
• They show you how to use PyTorch for different tasks, in a structured, incremental, and from first principles approach
• They build, step-by-step, not only the models themselves but also your understanding as I show you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.

What if I do not like the books? Can I get a refund?

Yes, you can! No questions asked! If you are not happy with your purchase, just reply to the download email within 30 days, and you will get your money back.

Can I share the book with my classmates?

If you'd like to share the book within your classmates, please choose the Classmates version on checkout.

What's inside

Volume I

• Gradient descent and PyTorch’s autograd
• Training loop, data loaders, mini-batches, and optimizers
• Binary classifiers, cross-entropy loss, and imbalanced datasets
• Decision boundaries, evaluation metrics, and data separability

Volume II

• Deep models, activation functions, and feature spaces
• Torchvision, datasets, models, and transforms
• Convolutional neural networks, dropout, and learning rate schedulers
• Transfer learning and fine-tuning popular models (ResNet, Inception, etc.)

Volume III

• Recurrent neural networks (RNN, GRU, and LSTM) and 1D convolutions
• Seq2Seq models, attention, masks, and positional encoding
• Transformers, layer normalization, and the Vision Transformer (ViT)
• BERT, GPT-2, word embeddings, and the HuggingFace library

Table of Contents

Volume I

• Chapter 0: Visualizing Gradient Descent
• Chapter 1: A Simple Regression Problem
• Chapter 2: Rethinking the Training Loop
• Chapter 2.1: Going Classy
• Chapter 3: A Simple Classification Problem

Volume II

• Chapter 4: Classifying Images
• Bonus Chapter: Feature Space
• Chapter 5: Convolutions
• Chapter 6: Rock, Paper, Scissors
• Chapter 7: Transfer Learning
• Chapter Extra: Vanishing and Exploding Gradients

Volume III

• Chapter 8: Sequences
• Chapter 9: Sequence-to-Sequence
• Chapter 10: Transform and Roll Out
• Chapter 11: Down the Yellow Brick Rabbit Hole

Testimonials

"I am usually really picky in choosing books about ML/DL but I have to tell you, this book was one of the best books I have ever invested in. I cannot thank you enough for writing a book that gives so much clarity on the explanations of the inner workings of many DL techniques. Thank you so much and I hope you come up with even better books on other ML topics in the future."

— Mahmud Hasan, Machine Learning Engineer at Micron Technology, Smart Manufacturing and AI

"As an author myself who've co-authored two books in Deep Learning & NLP space, I'm extremely impressed by Daniel's step-by-step pedagogical approach. Starting with a toy problem and gradually building abstractions on top of each other massively helps beginner to understand the nuts and bolts of each models and neural architectures be it basic or advanced! Daniel has justified "step-by-step" part from the title in a true sense. Highly recommended!"

— Nipun Nayan Sadvilkar, Lead Data Scientist & Author, DL & NLP Workshop

"We love the book as it’s so easy to read. The author uses simple words and avoids complex mathematical formulas, making the text feel like a conversation between friends."

— Zuzanna Sieja from DLabs.AI on 11 Books Every Data Scientist Must Read In 2022

"As for learning PyTorch and deep learning in general, Deep Learning with PyTorch Step-by-Step by Daniel Voigt Godoy is easily the best guide that I’ve found. I love how this huge hands-on tutorial it is structured, it starts from the ground level, then after showing the basic things, it goes straight into computer vision topics and in the end you get to know transformers and word embeddings, all of which play important part in the inner workings of CLIP."

— johanezz from deeplearn.art on Get started with making AI art in 2022

"This looks like it is very comprehensive, rich in detail and it might have been a huge investment of time and effort to write this. Kudos to you for making the convoluted stuff so enjoyable to read and easy to understand."

— Rishabh Kumar, Business Intelligence Analyst, on Linkedin

"This is the best book I have read in my entire life, in terms of richness, intuition, and practicality (also your puns)."

— @Teddy2kay1 on Twitter