rnelsonmwg / machine_learning

Tutorials/tips on machine learning related topics

Machine Learning

All codes related to software engineering, machine learning, artificial intelligence, and data science. I continuously update this repository to document my learnings for future references and to share the knowledge. Otherwise noted, I use Python3 on MacOS.

I make most frequent updates to the folders with * sign. Happy learning!

Directory

.
├── *Bash               # Everything related to bash/shell scripting
├── *Basics             # Python Basics 
├── *Classes            # Documentation of courses I take (more updates coming)
├── *Deep_Learning      # Everyhing Deep Learning, Tensorflow, Keras, etc 
├── *Machine_learning   # Everything Machine Learning
├── *NLP                # Anything Related to Natural Language Processing 
├── *RaspberryPi        # Useful knowledge and tools for using Raspberry Pi
├── *Research_Paper     # Documentation of research papers I read 
├── *Spark              # Everything Spark, PySpark, Distributed Computing

Go Watch

Here, I add interesting videos I've watched.

• 2019 Rework - Advancing State-of-the-art Image Recognition with Deep Learning on Hashtags
• 2019 Rework - Go-Explore: A New Type of Algorithm for Hard-exploration Problems
• MarI/O, MarioFlow
• AlphaGo Zero

Go Read

• 2019 OpenAI GPT-2

Never Stop Learning

Because field of machine learning is changing rapidly, it is important to keep up with new techniques and constantly learn. Good thing is that there is so much information online but it is easy to get lost. Here, I want to document some useful online resources or tips. I put mostly free resources.

Stay Up To Date:

• Tensorflow 2.0: Official Documentation, Tensorflow by Udacity, Tensorflow by DeepLearning.ai

Fundamentals

• Probability:
• Programming:
• Python Basics: Stanford CS231n - Python
• SQL: You can learn this on the job but you should know some basics for interview.
• Mathematics: Khan Academy's Linear Algebra

Intermediate

• Machine Learning: Andrew Ng's ML Course, Google Crash Course
• Deep Learning: DeepLearning.ai, fast.ai, theschool.ai
• Hadoop, Spark, Distributed Computing.

Advanced (or more in depth)

• Stanford's AI courses: ai.stanford.edu/courses
• Computer Vision: Stanford CS231n Home
• Reinforcement Learning: theschool.ai: move37, OpenAI

Textbooks / Reading

• Neural Network: Book: NNET and DL
• Text Analytics: https://web.stanford.edu/~jurafsky/slp3/
• Statistics and Machine Learning: Book: Intro to Statistical Learning
• Statistics: Book: OpenIntro Statistics
• TensorFlow: Hvass-Labs Tutorial

Optional

• Udacity's AB Testing

Raspberry Pi Projects

• PiNest
  • Demo Link: https://youtu.be/vsZC-V8orcY
  • Initial stage: https://youtu.be/lN0iA-JdOf0
  • Using Amazon Dash as a Camera Switch: (to be uploaded)
• Amazon Dash Hack: (to be uploaded)