Free online coursePyTorch Deep Learning Masterclass: CNNs, Transformers, GANs, Autoencoders and Diffusion

Course Description

Learn modern deep learning with PyTorch in a free, hands-on masterclass built for anyone who wants to go from core tensor operations to advanced generative and transformer-based models. This course guides you through the essential building blocks of neural networks, including classification and regression with multilayer perceptrons, choosing effective activation functions, and training reliable models with strong foundations in practical experimentation.

Move from fundamentals into computer vision workflows with convolutional networks, deeper architectures such as ResNets, transfer learning, and data augmentation strategies that help models generalize. You will also explore unsupervised approaches alongside practical projects such as object detection and U-Net style image segmentation, giving you a clear path from theory to implementation.

For generative modeling, the course covers autoencoders and variational autoencoders, GANs, and diffusion-based image generation, including latent diffusion with U-Net components and in-painting concepts. On the NLP side, you will build sequence models with RNNs and LSTMs for text classification, text generation, and sequence-to-sequence tasks, then implement attention mechanisms and progress to transformer architectures, including decoder-only next-token prediction and full encoder-decoder designs. You will also work with vision transformers, image captioning with transformers, and representation learning techniques such as semantic text clustering using pretrained models.

Beyond model training, you will gain exposure to interpretability with LIME, practical deployment considerations such as running PyTorch networks on a Raspberry Pi, and applied workflows like sentiment analysis with large language models. By the end, you will have a broad, practical understanding of how to build, train, and adapt deep learning systems in PyTorch across vision, text, and generative AI.