
{
  "title":"Schedule",
  "content":" SES # Topics Key Dates 1 Introduction to neural networks and deep learning (motivation, smart representations, layers, activations, architectures); training deep NNs (loss functions, gradient descent, regularization) 2 Training deep NNs (continued); introduction to Keras/Tensorflow (tensors, functional API, training, evaluation); application to tabular data 3 Deep learning for computer vision—building convolutional neural networks from scratch HW assignment #1 posted 4 Deep learning for computer vision—transfer learning and fine-tuning; introduction to HuggingFace 5 Deep learning for natural language—the basics (tokenization, n-grams, bag-of-words) HW assignment #1 due 6 Deep learning for natural language—embeddings HW assignment #2 posted 7 Deep learning for natural language—transformers Project proposal due 8 Deep learning for natural language—transformers, self-supervised learning 9 Generative AI—large language models (LLMs) and retrieval augmented generation (RAG) HW assignment #2 due 10 Generative AI—adapting LLMs with parameter-efficient fine-tuning 11 Generative AI—text-to-image models Projects due 12 Project presentations "}


