Deep Learning Materials by Deep Learning Wizard
Start Learning Now
Please head to www.deeplearningwizard.com to start learning! It is mobile/tablet friendly and open-source.
This repository contains all the notebooks and mkdocs markdown files of the tutorials covering machine learning, deep learning, scalable database, programming, data processing and data visualization powering the website.
Take note this is an early work in progress, do be patient as we gradually upload our guides.
Sections and Subsections
- Deep Learning and Deep Reinforcement Learning Tutorials (Libraries: Python, PyTorch, Gym, NumPy, Matplotlib and more)
- Course Progression
- Linear Regression
- Logistic Regression
- Feedforward Neural Network (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory Network (LSTM)
- Autoencoders (AE)
- Fully Connected Overcomplete Autoencoders
- Derivative, Gradient and Jacobian
- Forward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression)
- From Scratch Logistic Regression Classification
- From Scratch CNN Classification
- Learning Rate Scheduling
- Optimization Algorithms
- Weight Initialization and Activation Functions
- Supervised to Reinforcement Learning
- Markov Decision Processes and Bellman Equations
- Dynamic Programming
- Speed Optimization Basics Numba
- Machine Learning Tutorials (Libraries: Python, cuDF RAPIDS, cuML RAPIDS, pandas, numpy, scikit-learn and more)
- Programming Tutorials (Libraries: C++, Python, Bash and more)
- Scalable Database Tutorials (Libraries: Apache Cassandra, Bash, Python and more)
CPU to GPU Production-level Pipeline for AI
At Deep Learning Wizard, we cover the basics of some parts of the whole tech stack for production-level CPU/GPU-powered AI.
This AI pipeline is entirely based on open-source distributions.
This stack would get you started, and enable you to adjust the stack according to your needs.
About Deep Learning Wizard
We deploy a top-down approach that enables you to grasp deep learning theories and code easily and quickly. We have open-sourced all our materials through our Deep Learning Wizard Wikipedia. For visual learners, feel free to sign up for our video course and join thousands of deep learning wizards.
To this date, we have taught thousands of students across more than 120+ countries.
We are openly calling people to contribute to this repository for errors. Feel free to create a pull request.
Editors and Supporters
- Jie Fu, Editor (Postdoc in Montreal Institute for Learning Algorithms (MILA))
- Alfredo Canziani, Supporter (Postdoc in NYU under Yann Lecun)
- Marek Bardonski, Supporter (Global Head of Deep Learning, Sigmoidal)
Bugs and Improvements
Feel free to report bugs and improvements via issues. Or just simply try to pull to make any improvements/corrections.
If you find the materials useful, like the diagrams or content, feel free to cite this repository.