Week in Review
It felt great to get back to work. I love the work I'm doing and the pace I'm going at is definitely steady and consistent. I have a lot of puzzle pieces now that I need to work on putting together. Fortunately, this is the fun part-- the part where I get to build cool stuff while knowing what's actually going on behind the scenes.
It also felt great to add the Workout page to our website because both Abhay and I strongly feel that not having physical activity in your life is inexcusable. We hope we can inspire others to get in better shape because we also wholeheartedly believe it will improve their productivity. Weak men in positions of power is no better than fools in the battle field.
Progress Logs
January 1, 2024
Feels so good to be BACK back. Finally finished Andrew Ng's ML Specialization. Now I need to go back and do the labs or related projects to internalize the implementations. Coded up a NN from scratch with Abhay (need to do that a couple more time so I can start doing it without thinking). Went through Fast.AI's lecture 1. I'm excited about that because it's heavily practical, meaning EVERYTHING I learn is real-world. All in all, greart start to #DEEP24
January 2, 2024
Watched 2 Stat110 lectures (one of them was a review of the first midterm), watched 2 Fast.AI lectures (feels great to see NNs being taught in a different way), started on Breast Cancer Kaggle set with Abhay, and almost finished Course 2 labs from Andrew Ng (ML not Deep)
January 3, 2024
Watched Lecture 17 on Stat 110 did all labs for ML Specialization up until the last week of course 3. Spent most of my day starting and finishing course 1 of Deep Specialization. Most of it was review but it was cool seeing back propagation be derived. Staying locked in!
January 4, 2024
- Stat 110 Lecture 18
- Fast AI Lecture 4
- Went through almost all of the ML Labs (only have one more left)
- Implemented a NN from scratch using Course 1 of Deep Specialization (this took a while because I really dug in and wrote all the code and debugged)
- Finished 1 week of Course 2
It’s rejuvenating to see all the concepts I learned taking form in front of me. It feels worth it!
January 5, 2024
- Stat110 Lecture 19
- Fast AI Lectures 6 and 7 (I went thru the notebook for 6 and didn’t really follow with 7 because there was no note book, but definitely gonna iterate on decision trees once we start going in on projects)
- Finish Course 2 of Deep
- Went thru Fast AI’s implementation of titanic lin reg twice to really hammer down the steps necessary
I’m looking forward to CNNs and projects as wells as paper reviews. Step by step!
January 6, 2024
- Fast AI lectures 7 and 8; got a better look at Collaborative Filtering and an intro to CNNs. Recommendation systems are definitely crucial in the present AI landscape, so iterating over them is vital. The intro to CNNs was a good boost in my mindset of moving forward in my journey
- Finished Course 3 of Deep Learning Specialization
- Built a quick model for the Breast Cancer Dataset. I didn’t analyze the data at all, which is definitely something I need to learn in order to maximize my insight into data before I built models for them
All in all, not a perfect day, but definitely didn’t impact the cost function too much 😉
January 7, 2024
- Reviewed the past week with Abhay
- Worked on the Workout page of our website
Not much new today but it felt good to review and work on some web dev. We also set some goals for the next week and half before school starts. I think we’re in a great place and our pace is working.
Progress is 1 decision away everyday…
Changelog
The biggest change as a whole going into next week is a better sleep schedule. I disregarded my sleep this week and that cannot continue. I must take care of any tasks I can before lunch so I don't have to stay up late working. This next week will also be more practice focused and less theory oriented because I need to play with the puzzle pieces I have instead of getting new ones.
Goals for next week
- Above all else, do the labs I haven't done yet and make sure I can implement all of them well
- Finish Courses 4 and 5 in Deep Learning Specialization
- Finish Stat110
- KAGGLE KAGGLE KAGGLE!