Oreilly – Deep Learning with PyTorch video edition 2021-7

Apr 18, 2025 - 18:31

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Deep Learning with PyTorch video edition. This course teaches you how to build neural networks and deep learning systems using the PyTorch library. This hands-on book quickly walks you through building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire deep learning pipeline, including the PyTorch tensor API, loading data into Python, supervising training, and visualizing results. After covering the basics, the book takes you on a journey through larger projects. The main focus of the book is a neural network designed to detect cancer. You will explore methods for training networks with limited inputs and begin processing data to obtain some results. You will examine unreliable initial results and focus on how to detect and fix problems in your neural network. Finally, you will explore ways to improve your results by training with augmented data, making improvements to the model architecture, and performing other fine-tuning.

What you will learn:

Training deep neural networks
Implementing loss modules and functions
Using pre-trained models from PyTorch Hub
Review code samples in Jupyter Notebooks

This course is suitable for people who:

Python programmers are passionate about machine learning.

Course details: Deep Learning with PyTorch video edition

Publisher: Oreilly
Instructor: Eli Stevens , Luca Antiga, Thomas Viehmann
Training level: Beginner to advanced
Training duration: 15 hours and 32 minutes

Course headings

Part 1. Core PyTorch
Chapter 1. Introducing deep learning and the PyTorch Library
Chapter 1. Why PyTorch?
Chapter 1. An overview of how PyTorch supports deep learning projects
Chapter 1. Hardware and software requirements
Chapter 2. Pretrained networks
Chapter 2. Obtaining a pretrained network for image recognition
Chapter 2. Ready, set, almost run
Chapter 2. A pretrained model that fakes it until it makes it
Chapter 2. A network that turns horses into zebras
Chapter 2. A pretrained network that describes scenes
Chapter 3. It starts with a tensor
Chapter 3. Indexing tensors
Chapter 3. The tensor API
Chapter 3. Tensor metadata: Size, offset, and stride
Chapter 3. NumPy interoperability
Chapter 4. Real-world data representation using tensors
Chapter 4. 3D images: Volumetric data
Chapter 4. Representing scores
Chapter 4. Working with time series
Chapter 4. Ready for training
Chapter 4. One-hot encoding whole words
Chapter 4. Text embeddings as a blueprint
Chapter 5. The mechanics of learning
Chapter 5. Gathering some data
Chapter 5. Down along the gradient
Chapter 5. Normalizing inputs
Chapter 5. Optimizers a la carte
Chapter 5. Generalizing to the validation set
Chapter 6. Using a neural network to fit the data
Chapter 6. More activation functions
Chapter 6. The PyTorch nn module
Chapter 6. Finally a neural network
Chapter 7. Telling birds from airplanes: Learning from images
Chapter 7. Distinguishing birds from airplanes
Chapter 7. Representing the output as probabilities
Chapter 7. Training the classifier
Chapter 7. The limits of going fully connected
Chapter 8. Using convolutions to generalize
Chapter 8. Convolutions in action
Chapter 8. Looking further with depth and pooling
Chapter 8. Subclassing nn.Module
Chapter 8. Training our convnet
Chapter 8. Helping our model to converge and generalize: Regularization
Chapter 8. Going deeper to learn more complex structures: Depth
Chapter 8. Comparing the designs from this section
Part 2. Learning from images in the real world: Early detection of lung cancer
Chapter 9. Using PyTorch to fight cancer
Chapter 9. What is a CT scan, exactly?
Chapter 9. In more detail, we will do the following
Chapter 9. Why can’t we just throw data at a neural network until it works?
Chapter 9. What is a nodule?
Chapter 10. Combining data sources into a unified dataset
Chapter 10. Training and validation sets
Chapter 10. Loading individual CT scans
Chapter 10. Locating a nodule using the patient coordinate system
Chapter 10. A straightforward dataset implementation
Chapter 10. Constructing our dataset in LunaDataset.__init__
Chapter 11. Training a classification model to detect suspected tumors
Chapter 11. Pretraining setup and initialization
Chapter 11. Our first-pass neural network design
Chapter 11. The full model
Chapter 11. Outputting performance metrics
Chapter 11. Needed data for training
Chapter 11. Running TensorBoard
Chapter 11. Why isn’t the model learning to detect nodules?
Chapter 12. Improving training with metrics and augmentation
Chapter 12. Graphing the positives and negatives
Chapter 12. Our ultimate performance metric: The F1 score
Chapter 12. What does an ideal dataset look like?
Chapter 12. Samplers can reshape datasets
Chapter 12. Revisiting the problem of overfitting
Chapter 12. Seeing the improvement from data augmentation
Chapter 13. Using segmentation to find suspected nodules
Chapter 13. Semantic segmentation: Per-pixel classification
Chapter 13. Updating the model for segmentation
Chapter 13. Updating the dataset for segmentation
Chapter 13. Building the ground truth data
Chapter 13. Implementing Luna2dSegmentationDataset
Chapter 13. Designing our training and validation data
Chapter 13. Updating the training script for segmentation
Chapter 13. Getting images into TensorBoard
Chapter 13. Results
Chapter 14. End-to-end nodule analysis, and where to go next
Chapter 14. Bridging CT segmentation and nodule candidate classification
Chapter 14. Did we find a nodule? Classification to reduce false positives
Chapter 14. Quantitative validation
Chapter 14. Reusing preexisting weights: Fine-tuning
Chapter 14. What we see when we diagnose
Chapter 14. Beyond a single best model: Ensembling
Chapter 14. Conclusion
Part 3. Deployment
Chapter 15. Deploying to production
Chapter 15. Request batching
Chapter 15. Exporting models
Chapter 15. Interacting with the PyTorch JIT
Chapter 15. TorchScript
Chapter 15. LibTorch: PyTorch in C++
Chapter 15. C++ from the beginning: The C++ API
Chapter 15. Going mobile
Chapter 15. Improving efficiency: Model design and quantization

Course images

Deep Learning with PyTorch video edition

Sample course video

Installation Guide

After Extract, view with your favorite player.

Subtitles: None

Quality: 720p

Download link

Download Part 1 – 1 GB

Download Part 2 – 864 MB

File(s) password: www.downloadly.ir

File size

1.8 GB

Oreilly – Deep Learning with PyTorch video edition 2021-7