To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇
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I have always emphasized on the importance of mathematics in machine learning.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
Here is a compilation of resources (books, videos & papers) to get you going.
(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)
📘 Mathematics for Machine Learning
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
https://t.co/zSpp67kJSg
Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.
📘 Pattern Recognition and Machine Learning
by Christopher Bishop
Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.
📘 The Elements of Statistical Learning
by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie
Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...
📘 Probability Theory: The Logic of Science
by E. T. Jaynes
Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.
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"I lied about my basic beliefs in order to keep a prestigious job. Now that it will be zero-cost to me, I have a few things to say."
We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.
Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)
It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.
Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
As a dean of a major academic institution, I could not have said this. But I will now. Requiring such statements in applications for appointments and promotions is an affront to academic freedom, and diminishes the true value of diversity, equity of inclusion by trivializing it. https://t.co/NfcI5VLODi
— Jeffrey Flier (@jflier) November 10, 2018
We know that elite institutions like the one Flier was in (partial) charge of rely on irrelevant status markers like private school education, whiteness, legacy, and ability to charm an old white guy at an interview.
Harvard's discriminatory policies are becoming increasingly well known, across the political spectrum (see, e.g., the recent lawsuit on discrimination against East Asian applications.)
It's refreshing to hear a senior administrator admits to personally opposing policies that attempt to remedy these basic flaws. These are flaws that harm his institution's ability to do cutting-edge research and to serve the public.
Harvard is being eclipsed by institutions that have different ideas about how to run a 21st Century institution. Stanford, for one; the UC system; the "public Ivys".
