100 Days of Meanningful Learning challenge.

Motivation:

I have always wanted to be proficient at ML before I graduate Undergrad. I saw someone else doing this challenge on reddit and figured it would be fun! Since I am full time student not every single day needs to involve intense practice.

Rules:

• Engage with AI, ML, Evolutionary Computation, Cognitive Science, or Data Sci in some shape or form for the day. Even something as small as watching a single video or reading a news article.
• Link & share what was done for the day and quantifiy the amount done where possible.

What I am working through currently:

• Tutorials: Sentdex's Pytorch series
• Book: How to Create a Mind by Ray Kurweil
• Course: Introduction to Statistical Learning

Day 1 (1/28/2020)

• Led the paper discussion for AI@UCF on Applying Deep Learning to Right Whale Photo Identification

Day 2 (1/29/2020)

• Read part 1 of SpinningUp RL from OpenAI

Day 3 (1/30/2020)

• Listened to 2 episodes of TWIMLAI Podcast both centered around applications of Reinforcement Learning

Day 4 (1/31/2020)

• Watched 3 videos of Sentdex's Pytorch series
• Listened to Casuality episode of TWIMLAI podcast
• Watched half of Lex Friedman's Intro to Deep RL

Day 5 (2/1/2020)

• Read part 2 of SpinningUp RL from OpenAI
• Documented an approach for gathering data for Brain Beats music generation (The process would involve mapping MIDI files to Scribbletune's pattern language)
• First reading of Learning to Shoot in FPS games with Reinforcement Learning
• Worked through AI@UCF's Fall 2019 Reinforcement Learning Notebook

Day 6 (2/2/2020)

• Started reading Procedural Content Generation via Reinforcement Learning

Day 7 (2/3/2020)

• Finished reading Procedural Content Generation via Reinforcement Learning
• Watched 1st lecture from Stanfords Winter 2019 Reinforcement Learning course
• Listened to Helping Fish Farmers Feed the World with Deep Learning w/ Bryton Shang
• Watched 52 minutes of Fast.ai Practical Deep Learning for Coders lesson 01

Day 8 (2/4/2020)

• Finished Lex Friedman's Intro to Deep RL
• Listened to two episodes of Linear Digressions
• Listened to Microsoft Researc podcast - Malmo, Minecraft and machine learning with Dr. Katja Hofmann
• Participated in the paper discussion for AI@UCF on Learning to Shoot in FPS games with Reinforcement Learning

Day 9 (2/5/2020)

• Watched Henry AI Labs AI Weekly
• Watched Henry AI labs video on MuZero
• Watched Kaggle Reading Group: Learning from Dialogue after Deployment
• Developed a small LSTM using Brain.js to generate Scribbletune patterns for my Brain Beats senior design project (it performed quite badly)
• Watched Bayes theorem, and making probability intuitive
• Watched 1 episode of Sentdex's Pytorch series

Day 10 (2/6/2020)

• Listened to 2 episodes of TWIMLAI Podcast both involving MLOps and deploying models in production
• Read 22 pages from How to Create a Mind
• Listened to Interview with Juergen Schmidhuber on the AI podcast

Day 11 (2/7/2020)

• Watched 3 videos from Introduction to Statistical Learning

Day 12 (2/8/2020)

• Listened to the 1st episode of Brain Inspiried
• Watched 2 videos from Sentdex's Pytorch series

Day 13 (2/9/2020)

• Read 13 pages from How To Create a Mind
• Documented and solidifed the ML workflow for Brain Beats
• Listened to the 2nd episode of Brain Inspiried
• Began reading Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference

Day 14 (2/10/2020)

• Listened to the 3rd episode of Brain Inspiried
• Finished reading Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference
• Began writing reflective blogpost on the first 10 days
• Started working on a Feedforward Network that can solve simple math problems (Mainly data generation)
• Attended the Intro to CUDA workshop by KnightHacks

Day 15 (2/11/2020)

• Listened to the 4th and 5th episode of Brain Inspiried
• Participated in the paper discussion for AI@UCF on Pure Reasoning in 12-Month-Old Infants as Probabilistic Inference

Day 16 (2/12/2020)

• Debugging issues with my Neural Network for Math problems from Day 14

Day 17 (2/13/2020)

• Watched Deep Learning for Symbolic Mathematics by Kaggle reading Group

Day 18 (2/14/2020)

• Watched Why AlphaStar does not solve Gaming AIs problems
• Watched GPT Explained
• Watched How AlphaStar became a Starcraft Grandmaster

Day 19 (2/15/2020)

• Read Towards a Computational Model of Artificial Intuition and Decision Making (sidenote: Great paper!)

Day 20 (2/16/2020)

• Explained modern ML trends to my 63 year old mother (Meena, GPT2, StyleGANs)

Day 21 (2/17/2020)

• Discussed revisions for FAIble paper (my first ever paper that was accepted into Florida AI conference as a long paper!)
• Started read Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires

Day 22 (2/18/2020)

• Practiced for workshop I am cohosting on Azure's VideoIndexer for KnightHacks
• Participated in the paper discussion for AI@UCF on Towards a Computational Model of Artificial Intuition and Decision Making

Day 23 (2/19/2020)

• Finished reading Software Developers Learning Machine Learning: Motivations, Hurdles, and Desires