Mastering PyTorch: Deep Learning & Generative AI & CNN & SNN

About this course

Welcome to Mastering PyTorch: Deep Learning & Generative AI, a complete hands-on course designed to help you build a strong foundation in modern Artificial Intelligence using one of the world's most popular deep learning frameworks—PyTorch.

Whether you're a student, software developer, machine learning engineer, data scientist, researcher, or AI enthusiast, this course will take you step by step from the fundamentals of Deep Learning to advanced topics used in today's AI industry. You don't need prior experience with deep learning—only basic Python programming knowledge is recommended.

We begin by understanding Artificial Intelligence, Machine Learning, and Deep Learning before diving into PyTorch fundamentals, tensor operations, automatic differentiation (Autograd), and building Artificial Neural Networks (ANNs). You'll then learn industry best practices for training deep learning models, including DataLoaders, optimization techniques, regularization, batch normalization, dropout, hyperparameter tuning, and model evaluation.

As you progress, you'll build powerful Computer Vision applications using Convolutional Neural Networks (CNNs), learn transfer learning with popular pretrained models like ResNet, VGG, EfficientNet, MobileNet, and integrate OpenCV into your workflows.

The course then explores Sequential Neural Networks, including RNNs, LSTMs, and GRUs, before moving into one of the most important innovations in AI—the Transformer Architecture. You'll understand Attention Mechanisms, Self-Attention, Multi-Head Attention, Vision Transformers, and work with the Hugging Face ecosystem, including pretrained models, datasets, tokenizers, and inference pipelines.

Next, you'll learn how to fine-tune state-of-the-art Transformer models such as BERT, DistilBERT, and RoBERTa for your own applications. We then move into the exciting world of Generative AI by explorin

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