PyTorch Complete Training 2024: Learning PyTorch from Basics to Advanced

00:00 Introduction to Learning PyTorch from Basics to Advanced Complete Training 01:25 Introduction to PyTorch 10:16 Getting Started with PyTorch 18:39 Working with Tensors 29:09 Autograd and Dynamic Computation Graphs 36:40 Building Simple Neural Networks 46:56 Loading and Preprocessing Data 56:36 Model Evaluation and Validation 01:07:54 Advanced Neural Network Architectures 01:19:06 Transfer Learning and Fine-Tuning 01:27:18 Handling Complex Data 01:35:51 Model Deployment and Production 01:45:07 Debugging and Troubleshooting 01:55:23 Distributed Training and Performance Optimization 02:05:14 Custom Layers and Loss Functions 02:15:16 Research-oriented Techniques 02:25:39 Integration with Other Libraries 02:34:46 Contributing to PyTorch and Community Engagement This video is designed for developers, researchers, and machine learning enthusiasts who are looking to deepen their knowledge of PyTorch, one of the most popular deep learning frameworks. The video comprehensively covers advanced topics and best practices for working with PyTorch, making it ideal for individuals who already have a foundational understanding of machine learning and are aiming to refine their skills and contribute to the community. Throughout this session, participants will explore a variety of topics essential for effective machine learning model development and deployment using PyTorch. The session begins by delving into the creation of custom layers and loss functions, which are crucial for building models tailored to specific tasks. It also covers advanced activation functions like Swish, Mish, and GELU, as well as regularization techniques such as dropout and weight decay, which help improve model performance and prevent overfitting. The session then shifts focus to research-oriented techniques, emphasizing the importance of reproducibility in machine learning experiments. Participants will learn how to track experiments using tools like Neptune and Weights & Biases, optimize hyperparameters through grid search, random search, and Bayesian optimization, and stay updated with the latest research papers and conferences. Integration with other libraries is another key aspect of this session. Participants will discover how to integrate PyTorch with TensorFlow/Keras models, use OpenCV for computer vision tasks, and work with natural language processing libraries like spaCy and NLTK. This section equips learners with the skills to build comprehensive and multifaceted machine learning workflows. The session also highlights the importance of contributing to the PyTorch community, guiding participants through PyTorch’s contribution guidelines, and demonstrating how to submit bug fixes, documentation improvements, and new features. Additionally, it offers insights into engaging with the PyTorch community through forums, mailing lists, and social media. By the end of this session, participants will have gained a deep understanding of advanced PyTorch techniques, best practices for machine learning research, and methods for contributing to the PyTorch ecosystem. They will be equipped to create sophisticated, custom models, optimize and track their experiments, and actively participate in the broader machine learning community.