Get SH*T Done with PyTorch
$19.99
Minimum price
$39.99
Suggested price

Get SH*T Done with PyTorch

Solve Real-World Machine Learning Problems

About the Book

PyTorch is the best Deep Learning library there (currently) is, period! Doing ML with PyTorch feels like a superpower (of course, there are bad parts, too). Trust me, I have a book on TensorFlow and Keras! This opinion comes from my real-world experience, as a Machine Learning Engineer, and writer of numerous Machine Learning and Deep Learning tutorials.

This book skips the bull and goes straight into solving real-world problems. There is some theory, only where you need to connect the dots. You'll go from the basics of using PyTorch to solving Computer Vision, Natural Language, and Time Series problems with complete source code and runnable Jupyter notebooks. Explore the complete journey - from prototyping to deploying state-of-the-art models to production.

The examples are compatible with the latest versions of PyTorch and Torchvision.

Here’s what you’ll learn from this book:

  • Getting Started with PyTorch
  • Build Your First Neural Network with PyTorch
  • Transfer Learning for Image Classification using Torchvision
  • Time Series Forecasting with LSTMs for Daily Coronavirus Cases
  • Time Series Anomaly Detection using LSTM Autoencoders
  • Face Detection on Custom Dataset with Detectron2
  • Create Dataset for Sentiment Analysis by Scraping Google Play App Reviews
  • Sentiment Analysis with BERT and Transformers by Hugging Face
  • Deploy BERT for Sentiment Analysis as REST API using FastAPI

About the Author

Venelin Valkov
Venelin Valkov

Hello everybody,

My name is Venelin and I am thrilled to invite you on a journey through the amazing world of Machine Learning. 

I've been working on quite a lot of software projects in the past 10+ years, started my PhD program in Bayesian Statistics and been fascinated by Machine Learning in the past 5+ years (including some industry experience).

Table of Contents

  • 1. Getting Started with PyTorch
    • PyTorch ❤ NumPy
    • Tensors
    • Running on GPU
    • Common Issues
    • Conclusion
    • References
  • 2. Build Your First Neural Network with PyTorch
    • Data
    • Data Preprocessing
    • Building a Neural Network
    • Training
    • Saving the model
    • Evaluation
    • Conclusion
    • References
  • 3. Transfer Learning for Image Classification using Torchvision
    • Recognizing traffic signs
    • Building a dataset
    • Using a pre-trained model:
    • Adding class “unknown”
    • Summary
    • References
  • 4. Time Series Forecasting with LSTMs for Daily Coronavirus Cases
    • Novel Coronavirus (COVID-19)
    • Daily Cases Dataset
    • Data exploration
    • Preprocessing
    • Building a model
    • Training
    • Predicting daily cases
    • Use all data for training
    • Predicting future cases
    • Conclusion
    • References
  • 5. Time Series Anomaly Detection using LSTM Autoencoders
    • Data
    • Exploratory Data Analysis
    • LSTM Autoencoder
    • Anomaly Detection in ECG Data
    • Training
    • Saving the model
    • Choosing a threshold
    • Evaluation
    • Summary
    • References
  • 6. Face Detection on Custom Dataset with Detectron2
    • Detectron 2
    • Face Detection Data
    • Data Preprocessing
    • Face Detection with Detectron 2
    • Evaluating Object Detection Models
    • Finding Faces in Images
    • Conclusion
    • References
  • 7. Create Dataset for Sentiment Analysis by Scraping Google Play App Reviews
    • Setup
    • The Goal of the Dataset
    • Scraping App Information
    • Scraping App Reviews
    • Summary
    • References
  • 8. Sentiment Analysis with BERT and Transformers by Hugging Face
    • What is BERT?
    • Setup
    • Data Exploration
    • Data Preprocessing
    • Sentiment Classification with BERT and Hugging Face
    • Evaluation
    • Summary
    • References
  • 9. Deploy BERT for Sentiment Analysis as REST API using FastAPI
    • Project setup
    • Building a skeleton REST API
    • Adding our model
    • Putting everything together
    • Testing the API
    • Summary
    • References
  • 10. Object Detection on Custom Dataset with YOLO (v5)
    • Prerequisites
    • Build a dataset
    • Fine-tuning YOLO v5
    • Evaluation
    • Making predictions
    • Summary
    • References

Authors have earned$9,664,887writing, publishing and selling on Leanpub, earning 80% royalties while saving up to 25 million pounds of CO2 and up to 46,000 trees.

Learn more about writing on Leanpub

The Leanpub 45-day 100% Happiness Guarantee

Within 45 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

See full terms

Free Updates. DRM Free.

If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).

Most Leanpub books are available in PDF (for computers), EPUB (for phones and tablets) and MOBI (for Kindle). The formats that a book includes are shown at the top right corner of this page.

Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.

Learn more about Leanpub's ebook formats and where to read them

Write and Publish on Leanpub

You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses! Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks. Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. It really is that easy.

Learn more about writing on Leanpub