Complete Training: TensorFlow and PyTorch 2024

00:00 Welcome to Course on TensorFlow
00:48 Introduction to Machine Learning and TensorFlow
34:04 Installation and Setup
01:09:05 Tensors and Operations
01:19:32 Graphs and Sessions
01:31:47 Basic Neural Networks with TensorFlow
01:48:11 Customizing Models with Keras
02:03:35 Convolutional Neural Networks (CNNs)
02:17:34 Recurrent Neural Networks (RNNs)
02:30:00 Deploying TensorFlow Models
02:44:29 Distributed TensorFlow
03:01:34 TensorFlow Extended (TFX)
03:17:28 Real-world Applications
03:40:02 Hands-on Projects
04:01:08 Advanced Topics and Future Directions
04:17:23 Resources and Community
04:29:22 Wrapping Up TesnorFlow
04:39:11 Introduction to Learning PyTorch from Basics to Advanced Complete Training
04:40:36 Introduction to PyTorch
04:49:27 Getting Started with PyTorch
04:57:50 Working with Tensors
05:08:20 Autograd and Dynamic Computation Graphs
05:15:51 Building Simple Neural Networks
05:26:07 Loading and Preprocessing Data
05:35:47 Model Evaluation and Validation
05:47:05 Advanced Neural Network Architectures
05:58:17 Transfer Learning and Fine-Tuning
06:06:29 Handling Complex Data
06:15:02 Model Deployment and Production
06:24:18 Debugging and Troubleshooting
06:34:34 Distributed Training and Performance Optimization
06:44:25 Custom Layers and Loss Functions
06:54:27 Research-oriented Techniques
07:04:50 Integration with Other Libraries
07:13:57 Contributing to PyTorch and Community Engagement

This video is designed for developers, researchers, and machine learning enthusiasts aiming to deepen their knowledge of PyTorch and TensorFlow, two of the most popular deep learning frameworks. The content comprehensively covers advanced topics and best practices for working with both frameworks, making it ideal for individuals who already have a foundational understanding of machine learning and are looking to refine their skills, contribute to the community, and advance their careers.

Participants will explore essential topics for effective machine learning model development and deployment using PyTorch and TensorFlow. The session begins by delving into the creation of custom layers and loss functions, crucial for building models tailored to specific tasks, and discusses advanced activation functions like Swish, Mish, and GELU. It also covers regularization techniques such as dropout and weight decay to improve model performance and prevent overfitting.

In the context of TensorFlow, participants will engage with key concepts including tensors, computational graphs, and neural networks, and learn about deployment tools like TensorFlow Serving. The session also introduces TensorFlow Extended (TFX) for building end-to-end machine learning pipelines, equipping users to deploy models in production environments.

The session 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 equips learners with the skills to build comprehensive and multifaceted machine learning workflows.

The session also highlights the importance of contributing to the machine learning community, guiding participants through PyTorch’s and TensorFlow’s contribution guidelines, and demonstrating how to submit bug fixes, documentation improvements, and new features. Additionally, it offers insights into engaging with these communities through forums, mailing lists, and social media.

By the end of this session, participants will have gained a deep understanding of advanced techniques in PyTorch and TensorFlow, best practices for machine learning research, and methods for contributing to the broader machine learning ecosystem. They will be equipped to create sophisticated, custom models, optimize and track their experiments, and actively participate in the growing and evolving machine learning community.