Deep Learning and Neural Networks with PyTorch

A premium, complete and hands-on course on deep learning and neural networks built entirely on PyTorch, updated for 2026. You will start from why deep learning still matters, master tensors and automatic differentiation with autograd, and build up from linear regression to full multi-layer perceptrons. You will understand backpropagation and gradient descent from the inside, choose the right loss functions and optimizers (SGD, Adam, AdamW), write clean training loops with Datasets and DataLoaders, and defeat overfitting with regularization, dropout and batch normalization. From there you will build convolutional neural networks for images, work through RNNs and LSTMs and understand why Transformers replaced them, implement attention and embeddings, apply transfer learning and fine-tuning, train efficiently on GPU with CUDA and mixed precision, and take models to production with saving, loading and ONNX export. Every concept is paired with real, correct PyTorch code, and the course keeps a strong focus on the legal and ethical side of training data. Includes a comprehensive final assessment.