Alexander Xu

tech, ai ed, and my thoughts
math + cs @ stanford '30

[Alexander Xu @alexxubuilds]

DAY 12/30 of trying to become cracked at machine learning and sharing every resource I use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

spent today reimplementing a few machine learning algorithms from scratch, which has been super helpful to understanding how they work under the hood.

[Alexander Xu @alexxubuilds]

DAY 11/30 of trying to become cracked at machine learning and sharing every resource I use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

reviewed clustering with k-means and gaussian mixture models (probabilistic clusters) for day 11 of my series on becoming cracked at machine learning in 30 days.

[Alexander Xu @alexxubuilds]

DAY 10/30 of trying to become cracked at machine learning and sharing every resource I use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

studied support vector machines using @MITOCW lectures today. two surprises: 1. learning that maximizing the margin is closely linked to regularization. 2. you only need to compute the kernel function instead of transforming data. (day 10/30 of becoming cracked at ML)

[Alexander Xu @alexxubuilds]

DAY 9/30 of trying to become cracked at machine learning and sharing every resource I use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

reviewed bayes theorem and some bayesian statistics today. always a good reminder about how irrational human thinking can be (and how we could rationalize with bayes theorem)

[Alexander Xu @alexxubuilds]

DAY 8/30 of trying to become cracked at machine learning and sharing every resource i use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

studied the architecture of xgboost today (i'm trying to become cracked at machine learning in 30 days and this is day 8). the amount of optimizations is crazy, currently learning how it can be made distributed.

[Alexander Xu @alexxubuilds]

DAY 7/30 of trying to become cracked at machine learning and sharing every resource i use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

just finished studying gradient boosting (i'm trying to become cracked at machine learning in 30 days and this is day 7). really learned a lot about argmin optimization problems.

[Alexander Xu @alexxubuilds]

reading the source code for sklearn (or any big open-source library for that matter) is inspiring man

[Alexander Xu @alexxubuilds]

DAY 6/30 of trying to become cracked at machine learning and sharing every resource i use.

Follow to keep up to date on my journey! #tech #machinelearning

[Alexander Xu @alexxubuilds]

i just learned about random forests and adaboost. NEXT UP is gradient boosting.

i'm trying to become cracked at machine learning in 30 days and this was day 6 - follow to see how far i can get...

[Alexander Xu @alexxubuilds]

day 5/30 of becoming cracked at machine learning

[Alexander Xu @alexxubuilds]

im trying to become cracked at machine learning in 30 days. this was day 5, and i spent the day studying decision trees for regression and classification.

follow to join me on this journey and reply any suggestions!

[Alexander Xu @alexxubuilds]

Here's my game plan to learn all of the machine learning algorithms: sklearn's algorithm cheat sheet.

This is DAY 4/30 of becoming cracked at ML. Today, I started with regression algorithms (SGD, regularization, etc.) Follow to join me on this journey!

#machinelearning #tech

[Alexander Xu @alexxubuilds]

Can you become cracked at machine learning in 30 days? I'm trying to find out, this is DAY 3/30.

Today, I took a deep dive into sci-kit learn, which is the go-to library for traditional machine learning. Follow to join me on this journey!

#machinelearning #tech #sklearn

[Alexander Xu @alexxubuilds]

Can you become cracked at machine learning in 30 days? I'm trying to find out, this is DAY 2/30.

Today, I reviewed the fundamentals of machine learning via Andrew Ng's legendary supervised ML course. Follow to join me on this journey!

#machinelearning #tech #study