Get SH*T Done with PyTorch
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
Table of Contents
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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
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