2-Deep Learning Fundamentals
This is Lecture 2 of the series. Its role is to carry the machine learning framework from the previous lecture into the neural network context. It addresses several critical questions: why deep learning is needed, how a deep learning model is trained, and how to get started in an actual framework.
What This Lecture Covers
Judging from its table of contents, this lecture covers the essential topics you need to understand first when entering deep learning.
- Deep Learning Methods (Part 1): outlines the development of deep learning and explains why deep models are needed for many complex tasks.
- Deep Learning Methods (Part 2): describes the general steps for using deep learning in practice, introducing core mechanisms such as forward propagation and backpropagation.
- Getting Started with PaddlePaddle: provides an entry point to a specific framework, covering the environment, basic development workflow, and initial API usage.
- Course Practice: covers both regression and classification through house price prediction and handwritten digit recognition.
How to Study This
- The most important thing in this lecture is not memorizing a formula, but understanding the training loop: how input enters the network to produce output, how the loss is backpropagated, and how parameters are updated.
- If you are not yet familiar with deep learning frameworks, treat the framework section as a "minimal getting-started guide" and prioritize understanding the relationships between data, model, loss, and optimizer.
- The two practice examples correspond to regression and classification respectively -- it is best to go through both. This way, when you later study CNNs and RNNs, you will not only know model names but also understand how they work.