Welcome to the Deep Learning code repository!
I am a student currently exploring the field of Deep Learning. This repository serves as a code log of my learning journey, primarily following the classic tutorial Dive into Deep Learning (D2L) by Mu Li and others.
📢Since Mu Li's original code relies on the d2l library and is provided in Jupyter Notebook format, both of which have limited compatibility with newer Python versions, I will implement this code using standard Python and common deep learning libraries.
- 📚Learning & Practice : To reproduce and verify the theories from Dive into Deep Learning through code.
- 💾Code Archiving : To systematically save model code, experiment scripts, and learning notes generated during the study process.
- 🧠Knowledge Consolidation : To deepen the understanding of core concepts through hands-on practice.
- 📈Growth Record : To document the complete process and insights of learning Deep Learning from scratch.
- 📂Linear Regression
- 📂Softmax Regression
- 📂Multilayer Perceptrons (MLP)
- 📂Underfitting & Overfitting
- 📂Weight Decay
- 📂Dropout
- 📂Numerical Stability (Gradient Exploding & Vanishing)
- 📂House Prices - Advanced Regression Techniques
- 📂PyTorch Neural Network Fundamentals
- 📂Using GPUs
- 📂Convolutional Layer
- 📂Padding & Stride
- 📂Multiple Input and Output Channels
- 📂Pooling Layer
- 📂LeNet
- 📂AlexNet
- 📂VGG
- 📂NiN
- 📂GoogLeNet
- 📂Batch Normalization
- 📂ResNet
- 📂Classify Leaves
- 📂Multi-GPU Training
- 📂Image Augmentation
The code and content in this repository are primarily based on the following resources:
- Course: Dive into Deep Learning v2 by Aston Zhang, Zachary C. Lipton, Mu Li, and Alex J. Smola
- Video Course: Mu Li's official account on Bilibili. Click here to watch video : 跟李沐学AI - 动手学深度学习
- Framework: Using PyTorch for model implementation 🔥
This repository is mainly my personal learning note, but any form of communication and suggestions are welcome!
Happy Coding & Learning! 🚀