What To Do After Andrew Ng’s Course

A new article outlines steps for those who have completed Andrew Ng’s machine learning course, focusing on how to further develop understanding and skills in the field. It emphasizes the importance of moving beyond basic algorithms to mastering architectures and real-world data challenges.

The article suggests that many learners feel a disconnect after finishing the course, as they grasp foundational concepts like linear regression and gradient descent but struggle with modern terms such as transformers and embeddings. This gap is seen not as a failure of the course, but as a shift in the field that requires a deeper understanding of neural networks.

To rebuild one’s mental model for neural networks, the article recommends focusing on representation learning and understanding backpropagation in multilayer networks. It stresses the need to view neural networks as systems for reasoning and debugging rather than just a set of formulas. Practical exercises, such as reimplementing a neural network, can provide clarity on tensor shapes and activation choices.

In transitioning from algorithms to architectures, the article highlights the importance of understanding the underlying assumptions of different models. It encourages learners to view architectures as hypotheses about data structure, rather than merely memorizing diagrams. It also advises thinking in terms of pipelines and the interactions between components rather than isolated models.

The article further discusses the challenges of working with real data, noting that modern machine learning often deals with messy and biased datasets. It emphasizes the importance of statistical and visual data inspection, as well as the need to be comfortable with experiments that do not yield clean results.

Lastly, the article explains how to understand language models without viewing them as mysterious. It encourages focusing on foundational concepts such as embeddings and attention, which can demystify the functioning of large language models. Fine-tuning and prompting should be approached after mastering these basics.