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12 courses
Bring dataset preparation, LoRA fine-tuning and evaluation together in a project-focused capstone that connects model development with a defined community need.
Explore compute estimates, GPU memory, mixed precision and gradient accumulation while considering the resource and environmental trade-offs of model training.
Compare pre-training and fine-tuning, explore full-parameter adaptation and LoRA, and examine the data and governance choices behind instruction-following models.
Explore attention, positional embeddings and Transformer components through implementation exercises, architectural limits and community-focused reflection.
Learn to interpret loss curves, design multilayer perceptrons and investigate generalization through Keras exercises and responsible model reflection.
Explore tokenization, byte pair encoding and embeddings, then connect language-data representation with privacy, documentation and community context.
Explore n-grams, Transformer models and the Keras training workflow while connecting your first small language model with a meaningful problem.
Study prediction uncertainty, overconfidence and reliability methods in Google’s free reading-based deep learning course.
Learn how encoder-decoder models handle sequence tasks and follow a TensorFlow poetry-generation walkthrough in Google’s free short course.
Explore how image captioning combines an encoder and decoder, then follow Google’s free overview, model walkthrough and knowledge quiz.
Understand Transformer architecture, self-attention and BERT through a free Google course with an overview, recorded walkthrough and quiz.
Understand how attention helps neural networks focus on relevant information in a short, free Google Skills course with a knowledge check.