# Machine Learning CS299

* [Brandon McKinzie](https://mckinziebrandon.me/notes/) for CS299 etc.
* [PythonAndr](https://pythonandr.com/2015/11/25/supplementary-material-to-andrew-ngs-machine-learning-mooc/) for CS299 by Andrew Ng

## Deep Learning Specialization by Andrew Ng

* [Kulbear](https://github.com/Kulbear/deep-learning-coursera)
* [enggen](https://github.com/enggen/Deep-Learning-Coursera)

## Machine Learning Course

* [Machine Learning at CUNI NZ](https://ufal.mff.cuni.cz/courses/npfl129/2021-winter#lectures)
* [Elements of AI](https://course.elementsofai.com/)
* [TensorFlow, Keras and deep learning, without a PhD](https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist#0), [Github](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd), License: Apache
* [CS231n Stanford](https://cs231n.stanford.edu/syllabus.html), [CS231n Github](https://cs231n.github.io/), [CS231n GIthub Source](https://github.com/cs231n/cs231n.github.io) License: MIT
  * [Set Up Google Cloud Platform for Machine Learning](https://github.com/cs231n/gcloud)
  * [Slides](https://cs231n.stanford.edu/slides/)
  * [Schedule](https://cs231n.stanford.edu/schedule.html)
* [Schedule - EECS 498-007 / 598-005: Deep Learning for Computer Vision](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/schedule.html)
* [AI-Sys Sp19](https://ucbrise.github.io/cs294-ai-sys-sp19/) [ucbrise/cs294-ai-sys-sp19: CS294; AI For Systems and Systems For AI](https://github.com/ucbrise/cs294-ai-sys-sp19)
* [CS182/282A Designing, Visualizing and Understanding Deep Neural Networks Spring 2020: Designing, Visualizing and Understanding Deep Neural Networks (Spring 2020)](https://bcourses.berkeley.edu/courses/1487769/pages/cs-l-w-182-slash-282a-designing-visualizing-and-understanding-deep-neural-networks-spring-2020)
  * [mj-hwang/cs182-deep-neural-network: CV, NLP, and RL projects in CS 182: Designing, Visualizing and Understanding DNNs (spring 2020)](https://github.com/mj-hwang/cs182-deep-neural-network)
* [MIT Deep Learning 6.S191](https://introtodeeplearning.com/#schedule)
  * [aamini/introtodeeplearning: Lab Materials for MIT 6.S191: Introduction to Deep Learning](https://github.com/aamini/introtodeeplearning)

## Practical Deep Learning

* [rajatkb/Practical-Deep-Learning](https://github.com/rajatkb/Practical-Deep-Learning)
* [sjchoi86/dl\_tutorials](https://github.com/sjchoi86/dl_tutorials): Deep Learning Presentation and Tutorial License: MIT

### Visualizing Backpropagation

* [HMKCode](https://hmkcode.com/ai/backpropagation-step-by-step/)
* [MattMazur](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/)
* [JeremyJordan](https://www.jeremyjordan.me/neural-networks-training/)

### Machine Learning Course

* [Course Materials Academy Neuromatch](https://academy.neuromatch.io/nma2020/course-materials)
  * [course-content/README.md at master · NeuromatchAcademy/course-content](https://github.com/NeuromatchAcademy/course-content/blob/master/tutorials/README.md)
* [COMS W4995 Applied Machine Learning Spring 2020 - Schedule - Andreas C. Müller - Associate Research Scientist](https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/),
  * [amueller/COMS4995-s19: COMS W4995 Applied Machine Learning - Spring 19](https://github.com/amueller/COMS4995-s19) License Public Domain
  * [amueller/introduction\_to\_ml\_with\_python: Notebooks and code for the book "Introduction to Machine Learning with Python"](https://github.com/amueller/introduction_to_ml_with_python)
  * [Youtube](https://www.youtube.com/playlist?list=PL_pVmAaAnxIRnSw6wiCpSvshFyCREZmlM)
  * [Youtube 2019](https://www.youtube.com/watch?v=Qd68h4UGlNY\&list=PL_pVmAaAnxIQGzQS2oI3OWEPT-dpmwTfA)