Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A lecture note for understanding deep learning
| Date | Stars |
|---|---|
| 2026-08-04 | 463 |
| 2026-08-06 | 465 |
| 2026-08-13 | 465 |
| 2026-08-18 | 465 |
| 2026-08-22 | 466 |
| 2026-08-26 | 467 |
| 2026-08-31 | 468 |
| 2026-09-01 | 467 |
| 2026-09-06 | 468 |
| 2026-09-07 | 468 |
| 2026-09-08 | 470 |
| 2026-09-11 | 471 |
| 2026-09-14 | 472 |
| 2026-09-17 | 476 |
| 2026-09-25 | 477 |
| 2026-10-06 | 476 |
| 2026-10-08 | 477 |
| 2026-10-09 | 478 |
| 2026-10-14 | 479 |
| 2026-10-15 | 480 |
| 2026-10-16 | 479 |
| 2026-10-26 | 480 |
| 2026-10-27 | 481 |
| 2026-10-30 | 482 |
| 2026-10-31 | 483 |
| 2026-11-04 | 483 |
Today
— stars today
This week
+2 stars this week
This month
+6 stars this month
Momentum
2.0
growth rate 0.42%/day
# Introduction to Deep Learning Phenomena
This repository contains lecture notes and related resources on deep learning, using observed phenomena as a starting point for understanding its core concepts. It is intended for researchers, practitioners, students, and others interested in deep learning.
## Course Videos
The complete course videos by Zhiqin Xu are available on [Bilibili](https://space.bilibili.com/95975441/channel/seriesdetail?sid=3749682), covering both introductory and advanced topics in deep learning.
## Updates (June 28, 2025)
1 Mainly corrected wording and expression errors.
2 Added Chapter 15, "Solving Differential Equations with Neural Networks."
## Updates (November 13, 2024)
### 1. Experiments
- **New materials:** Added an experimental section with detailed lab notes and corresponding Jupyter notebooks. These materials provide practical deep learning examples and data analyses to reinforce the concepts in the lecture notes.
### 2. The Effect of Initialization on Reasoning
- **Theory-driven applications:** Added a section discussing how initialization affects reasoning performance. It applies condensation theory to large language models, illustrating how theoretical insights can inform practical applications and connecting theory with practice.
### 3. Revisions to the Chapter on Models
- **Variational autoencoder updates:** Improved the section on variational autoencoders.
## Chapter 1: Introduction to Deep Learning
### **Overview**
This chapter explains how neural networks work (backpropagation and gradient descent), the evolution of optimization algorithms, and the geometry of loss landscapes. It also explores the tension between deep learning and classical statistical learning theory: the **generalization puzzle**, in which overparameterized models can achieve better generalization. The chapter introduces **implicit bias** as a central concept for explaining this phenomenon and concludes by proposing a **phenomenon-driven** research paradigm.
---
### **Outline**
<details>
<summary><b>1. The Essence of Deep Learning</b></summary>
* **Data Fitting:**
* Mathematically, deep learning seeks a function model that approximates the underlying patterns in data (the target function).
* The goal is to learn a mapping from the input space to the output space that performs well on unseen test data (generalization).
* **Neural Network Architectures:**
* **How neurons work:** Inspired by biological neurons, artificial neurons process information through a weighted linear combination ($w^Tx + b$) and a nonlinear activation function (ReLU, sigmoid, tanh, etc.).
* **Multilayer structure:** A single-layer perceptron can solve only linearly separable problems (and therefore cannot solve XOR). By stacking nonlinear transformations, multilayer neural networks gain powerful expressive capabilities (the universal approximation theorem).
</details>
<details>
<summary><b>2. Model Training and Optimization</b></summary>
* **Loss Functions:**
* Measure the discrepancy between model predictions and ground-truth labels.
* **Regression:** Common choices include mean squared error (MSE) and mean absolute error (MAE).
* **Classification:** Cross-entropy is commonly used, often with softmax to convert outputs into a probability distribution.
* **Loss Landscapes:**
* Describe the geometry of the loss function as model parameters vary.
* Deep learning loss landscapes are typically nonconvex, high-dimensional surfaces with many local minima and saddle points.
* **Optimization Methods:**
* **Backpropagation:** Uses the chain rule to compute gradients efficiently and determine parameter update directions.
* **Gradient descent (GD) and its variants:**
* **Stochastic gradient descent (SGD):** Uses a small number of samples at each step, reducing computation and introducing stochastic noise that can help escape local optima.
*Excerpt of 62,948 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:fc8fd67b80cc54da, llm:Repository description: 'A lecture note for understanding deep learning' (Jupyter Notebook)
matched fp:fc8fd67b80cc54da, llm:Repository description: 'A lecture note for understanding deep learning' (Jupyter Notebook)