Become an AI Researcher: Math, PyTorch, LLMs & Transformers (Video Course) is a practical, video-first certification that takes you from Python basics to reading papers and building GPT-style models. You'll develop the math intuition that makes training work, the PyTorch chops to fix shape bugs and move fast, and career benefits like Increased Productivity, Competitive Advantage, Improved Decision-Making, Adaptability and Growth, Higher Income Potential, and a Future-Proof Career. If you're ready to contribute,not just watch,enroll and turn curiosity into working systems and research-ready skills.
This certification covers the following topics:
- Python-to-PyTorch fundamentals: tensors, indexing, slicing, and boolean masking
- Tensor shapes and operations: reshape, view, squeeze/unsqueeze, transpose, and permute
- Core math for deep learning: functions, derivatives, gradients, vectors, and matrices
- Probability for reasoning under uncertainty
- Neural network building blocks: single neurons, activation functions, and forward passes
- Loss functions, optimization, and a minimal training loop in PyTorch
- Autograd and backpropagation for end-to-end differentiation
- Attention mechanics: Q, K, V, scaled dot-product, softmax, and causal masking
- Transformer architecture: multi-head attention, positional embeddings, residual connections, layer normalization, and feed-forward blocks
- Decoder-only GPT stack and step-by-step training (data, batching, evaluation)
