Deep Learning Materials by Deep Learning Wizard
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Please head to www.deeplearningwizard.com to start learning! It is mobile/tablet friendly and open-source.
Repository Details
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
- Matrices
- Gradients
- 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.
Contribution
We are openly calling people to contribute to this repository for errors. Feel free to create a pull request.
Main Contributor
Editors and Supporters
- Jie Fu, Editor (Postdoc in Montreal Institute for Learning Algorithms (MILA))
- Alfredo Canziani, Supporter (Assistant Prof in NYU under Yann Lecun)
- Marek Bardonski, Supporter (Managing Partner, AIRev)
Bugs and Improvements
Feel free to report bugs and improvements via issues. Or just simply try to pull to make any improvements/corrections.
Social Media
Citation
If you find the materials useful, like the diagrams or content, feel free to cite this repository.
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人工神经网络（ANN）是一种高效的计算系统，其中心主题借鉴了生物神经网络的类比。 神经网络是机器学习的一种模型。 在20世纪80年代中期和90年代初期，在神经网络中进行了许多重要的建筑改进。 在本章中，您将了解有关深度学习的更多信息，这是一种人工智能的方法。 深度学习源自十年来爆炸性的计算增长，成为该领域的一个重要竞争者。 因此，深度学习是一种特殊的机器学习，其算法受到人脑结构和功能的启发。 机器
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深度学习 我们可以在Personal Computer上完成庞大的任务 深度学习是一种适应于各类问题的万能药 神经网络 神经网络出现于80年代，但当时计算机运行慢，数据集很小，神经网络不适用 现在神经网络回来了，因为能够进行GPU计算，可用使用的数据集也变大 分类 分类的一些讨论可以在这个项目里看到 Machine Learning不仅是Classification！但分类是机器学习的核心。 学会
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When Bitcoin meets Artificial Intelligence Exploiting Bitcoin prices patterns with Deep Learning. Like OpenAI, we train our models on raw pixel data. Exactly how an experienced human would see the cur
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Awesome Deep Learning Table of Contents Books Courses Videos and Lectures Papers Tutorials Researchers Websites Datasets Conferences Frameworks Tools Miscellaneous Contributing Books Deep Learning by
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Deep Learning Specialization Projects from the Deep Learning Specialization from deeplearning.ai offered by Coursera. Instructor: Andrew Ng Master Deep Learning and Break Into AI If you want to break
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這裡紀錄了我在學習深度學習時蒐集的一些線上資源。內容由淺入深，而且會不斷更新，希望能幫助你順利地開始學習：） 本文章節 遊玩空間 線上課程 實用工具 其他教材 優質文章 經典論文 其他整理 遊玩空間 這節列舉了一些透過瀏覽器就能馬上開始遊玩 / 體驗深度學習的應用。作為這些應用的使用者，你可以先高層次、直觀地了解深度學習能做些什麼。之後有興趣再進一步了解背後原理。 這小節最適合： 想要快速體會深度