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From Basics to Advanced Deep Learning Training

What you will learn

Understand PyTorch fundamentals, including tensors and computation graphs

Build and train neural networks using PyTorch’s nn_Module

Preprocess and load datasets with DataLoaders and custom datasets

Implement advanced architectures like CNNs, RNNs, and Transformers

Perform transfer learning and fine-tune pre-trained models

Optimize models using hyperparameter tuning and regularization

Deploy trained models using TorchScript and cloud services

Debug and troubleshoot deep learning models effectively

Develop custom layers, loss functions, and models

Collaborate with the PyTorch community and contribute to open-source projects

Why take this course?

The “Mastering PyTorch: From Basics to Advanced Deep Learning Training” course is a complete learning journey designed for beginners and professionals aiming to excel in artificial intelligence and deep learning. This course begins with the fundamentals of PyTorch, covering essential topics such as tensor operations, automatic differentiation, and building neural networks from scratch. Learners will gain a deep understanding of how PyTorch’s dynamic computation graph works, enabling flexible model creation and troubleshooting.

As the course progresses, students will explore advanced topics, including complex neural network architectures such as CNNs, RNNs, and Transformers. It also dives into transfer learning, custom layers, loss functions, and model optimization techniques. Learners will practice building real-world projects, such as image classifiers, NLP-based sentiment analyzers, and GAN-powered applications.


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