Deep Learning and Neural Networks with PyTorch and Transformers

Deep learning is driving the most exciting breakthroughs in technology today, but understanding how neural networks actually work can feel overwhelming. This text-based course demystifies the mathematical foundations and guides you through building your own models from scratch. You will transition from a curious beginner to a confident practitioner capable of designing, training, and optimizing neural networks. By exploring both the theoretical mechanics and practical implementations, you will gain a deep, intuitive grasp of how modern AI models process information and generate results. What you'll learn: - Understand the core mechanics of neural networks, backpropagation, and mathematical activation functions. - Configure loss functions, weight initializations, and advanced optimization algorithms like Adam. - Apply regularization techniques such as Dropout and Batch Normalization to prevent overfitting. - Build deep learning models step-by-step using the PyTorch library. - Explore modern Transformer architectures, including the foundational concepts behind BERT and GPT. - Discover how neural networks integrate into contemporary AI workflows like retrieval-augmented generation (RAG). The curriculum begins with essential terminology and the basic mathematics of neural networks before moving into hands-on PyTorch implementations. You will progress from simple single-layer networks to complex multi-layer architectures and modern language models through clear written explanations and structured code analysis. This course is designed for aspiring data scientists, developers, and tech enthusiasts who are new to deep learning. No prior experience with neural networks is required, though a basic familiarity with Python is helpful. Start reading today to unlock the inner workings of modern artificial intelligence.