This project aims to create a collection of Jupyter notebooks discussing important topics in Deep Learning.
- Linear Classifier
- Linear Regression
- Support Vector Machine
- Gradient Descent (No framework)
- Forward props & Back props
- Multilayer Perceptron
- Neural Net (No framework, only numpy)
- vectors & tensors
- Different neural net layers (dense, dropout, max-pooling layers)
- Data preprocessing (house price dataset, catvsdog, IMDB)
- K Mean Clustering
- CNN (Pytorch, TF, Keras) -> classify whales photos
- Callbacks (early stopping and etc)
- TF Dashboard
- Training model in the cloud
- Sentiment Analysis (binary classification)
- Sentiment analysis (multi-classification)
- Word2vec
- Hyperparamter Tuning
- Transfer Learning
- Generate song lyrics and stories
- Hyperparameter tuning
- Feature engineering
- Linear regression Kaggle competition using the above knowledge
- Generative Models
- GAN basic
- StarGAN
- CycleGAN
- Reinforcement learning
- Basic Python Knowledge
- Some Machine Learning
- Basic idea about Deep Learning
- Anaconda
Anaconda is a package management platform Data Scientists that lets you easily manage and install dependencies in cross-platform manner. It also ships with Jupyter Notebook, which plays a critical role in order to contribute to this project.
For more detail about why you should use Anaconda? https://www.quora.com/Why-should-I-use-anaconda-instead-of-traditional-Python-distributions-for-data-science
Installation per platform:
- Docker
Details will be added shortly
- Join the Discord channel https://discord.gg/MAMPnmm
- Goto #request-to-join channel and post your Github Account name!
- Once you are granted with access to the project, please create a git branch with your name
- Complete each topic and challenge in order of number sequence
- for example: 000-Linear-Classification -> 001-Linear-Regression
- Reference existing code submissions from contributors or Wiki pages
- I will post Jupyter Notebooks with sample code or challenges to complete