A classical online course by Professor Gilbert Strang based on his popular textbook "Introduction to Linear Algebra". Learn about matrix theory and systems of equations.
https://t.co/GarhvVxXhG
Recently, I shared a list of some courses that were useful in my transition to machine learning.
While I took most of the courses in person, there are some alternative online courses you can check out.
Here is a thread of a few interesting online courses based on the list:
A classical online course by Professor Gilbert Strang based on his popular textbook "Introduction to Linear Algebra". Learn about matrix theory and systems of equations.
https://t.co/GarhvVxXhG
Learn about the geometry of complex numbers.
https://t.co/DgMuMCgAhr
Pay close attention to the chain rule as it's heavily referenced in machine learning, specifically when discussing optimization. This course is part of a specialization called MathTrackX. I recommend checking that as well.
https://t.co/H4hK05t44b
When you are working with machine learning algorithms applied to data you are dealing with information processing which in essence relies on ideas from information theory such as entropy. This course should provide the basics.
https://t.co/ETeMiwTry1
The courses in this specialization provide a great overview of data mining techniques used for structured and unstructured data.
https://t.co/oGzoOGOMnU
In machine learning, we are programming sophisticated algorithms and it's important to understand key concepts in this subject before jumping straight into ML algorithms. In general, an Algorithms course builds a strong CS foundation.
https://t.co/bdlXphoJud
Note: Includes courses for multivariate calculus and linear algebra. One of my favorite courses due to the quality of lectures and focused topics.
https://t.co/3Uf3iuni3z
This course is focused on the basics of statistics which is important when dealing with uncertainties, modeling, inference, etc. Although the courses focus on Python, there are other options using R as well.
https://t.co/yZOUQBTZNI
Probability can become a difficult topic but it's a core concept of building probabilistic prediction models. This course can provide an intuitive introduction to core topics like conditional probability.
https://t.co/sGirM58T9p
More from elvis
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.
The past month I've been writing detailed notes for the first 15 lectures of Stanford's NLP with Deep Learning. Notes contain code, equations, practical tips, references, etc.
As I tidy the notes, I need to figure out how to best publish them. Here are the topics covered so far:
I know there are a lot of you interested in these from what I gathered 1 month ago. I want to make sure they are high quality before publishing, so I will spend some time working on that. Stay
Below is the course I've been auditing. My advice is you take it slow, there are some advanced concepts in the lectures. It took me 1 month (~3 hrs a day) to take rough notes for the first 15 lectures. Note that this is one semester of
I'm super excited about this project because my plan is to make the content more accessible so that a beginner can consume it more easily. It's tiring but I will keep at it because I know many of you will enjoy and find them useful. More announcements coming soon!
NLP is evolving so fast, so one idea with these notes is to create a live document that could be easily maintained by the community. Something like what we did before with NLP Overview: https://t.co/Y8Z1Svjn24
Let me know if you have any thoughts on this?
As I tidy the notes, I need to figure out how to best publish them. Here are the topics covered so far:
I know there are a lot of you interested in these from what I gathered 1 month ago. I want to make sure they are high quality before publishing, so I will spend some time working on that. Stay
I've been writing notes for the latest Deep Learning for NLP course by Stanford.
— elvis (@omarsar0) January 14, 2022
For fun, I also started to add my own code snippets into the notes. I think this is a more efficient way to study: theory + code.
Plan to share these notes soon. Stay tuned! pic.twitter.com/hWzZDORbl6
Below is the course I've been auditing. My advice is you take it slow, there are some advanced concepts in the lectures. It took me 1 month (~3 hrs a day) to take rough notes for the first 15 lectures. Note that this is one semester of
I'm super excited about this project because my plan is to make the content more accessible so that a beginner can consume it more easily. It's tiring but I will keep at it because I know many of you will enjoy and find them useful. More announcements coming soon!
NLP is evolving so fast, so one idea with these notes is to create a live document that could be easily maintained by the community. Something like what we did before with NLP Overview: https://t.co/Y8Z1Svjn24
Let me know if you have any thoughts on this?
More from Education
Last month I presented seven sentences in seven different languages, all written in a form of the Chinese-character script. The challenge was to identify the languages and, if possible, provide a
Here again are those seven sentences:
1) 他的剑从船上掉到河里去
2) 於世𡗉番𧡊哭唭𢆥尼歲㐌外四𨑮
3) 入良沙寢矣見昆腳烏伊四是良羅
4) 佢而家喺邊喥呀
5) 夜久毛多都伊豆毛夜幣賀岐都麻碁微爾夜幣賀岐都久流曾能夜幣賀岐袁
6) 其劍自舟中墜於水
7) 今天愛晚特語兔吃二魚佛午飯
Six of those seven sentences are historically attested. One is not: I invented #7. I’m going to dive into an exploration of that seventh sentence in today’s thread.
Sentence #7 is an English-language sentence written sinographically — that is, using graphs that originate in the Chinese script. I didn’t do this for fun (even though it is fun), or as a proposal for a new way to write
I did it as a thought experiment. Why? Because thinking about how the modern Chinese script might be adapted to write modern English can give us valuable insights into historical instances of script borrowing, like those that took place centuries ago in Japan, Korea, and Vietnam.
\U0001f17b\U0001f130\U0001f17d\U0001f176\U0001f184\U0001f130\U0001f176\U0001f174 \U0001f180\U0001f184\U0001f178\U0001f189
— zev handel (@ZevHandel) December 17, 2020
The following sentences are in seven different languages, all written in Chinese-character script (or a modification of it). Can you identify the languages?
Sentences are in thread.
(1/3)
Here again are those seven sentences:
1) 他的剑从船上掉到河里去
2) 於世𡗉番𧡊哭唭𢆥尼歲㐌外四𨑮
3) 入良沙寢矣見昆腳烏伊四是良羅
4) 佢而家喺邊喥呀
5) 夜久毛多都伊豆毛夜幣賀岐都麻碁微爾夜幣賀岐都久流曾能夜幣賀岐袁
6) 其劍自舟中墜於水
7) 今天愛晚特語兔吃二魚佛午飯
Six of those seven sentences are historically attested. One is not: I invented #7. I’m going to dive into an exploration of that seventh sentence in today’s thread.
Sentence #7 is an English-language sentence written sinographically — that is, using graphs that originate in the Chinese script. I didn’t do this for fun (even though it is fun), or as a proposal for a new way to write
7) \u4eca\u5929\u611b\u665a\u7279\u8a9e\u5154\u5403\u4e8c\u9b5a\u4f5b\u5348\u98ef \u2013 Modern English
— zev handel (@ZevHandel) December 21, 2020
Today I want you to each two fish for lunch.
That this sentence is a written form of English is undeniable, as the sentence is made up entirely of English words following the rules of English grammar. 23/
I did it as a thought experiment. Why? Because thinking about how the modern Chinese script might be adapted to write modern English can give us valuable insights into historical instances of script borrowing, like those that took place centuries ago in Japan, Korea, and Vietnam.
I get asked a lot how you can improve your skills and chances of getting a job as a developer. Best way is to work on a real-world project, deploy it, make it open-source, get feedback from others, share your knowledge, rinse, repeat.
Here are my top 7 project ideas. Thread 👇
1. 📊 Build an embeddable user feedback form (clone of https://t.co/xFHvT7iFEf) . Have a top notch design, fully working, minimal bugs, open-source, deploy it free on Heroku / Netlify / Vercel. If you can spare $11, buy a domain. Share with the whole world when done.
2. 🚀 Build a product roadmap SAAS.(https://t.co/Rq9DBeCMlh) Users can create new projects, create different stages for their projects. The community can submit project ideas, vote on existing ideas. Project owners pay a monthly fee per project.
3. ⛈️ Build a digital marketplace. (https://t.co/BWd1aeWMt5) Sellers can upload digital products for sale. Customers can purchase digital products and securely download. Sellers are paid out at the end of every month. Don't make it complicated, implement a great design.
4. 👨🏭 Build a job board software (https://t.co/EjWoMyqi9H). Companies can post jobs for a price, providing a link to the job application form. Jobs can be highlighted as urgent for an additional price.
Here are my top 7 project ideas. Thread 👇
1. 📊 Build an embeddable user feedback form (clone of https://t.co/xFHvT7iFEf) . Have a top notch design, fully working, minimal bugs, open-source, deploy it free on Heroku / Netlify / Vercel. If you can spare $11, buy a domain. Share with the whole world when done.
2. 🚀 Build a product roadmap SAAS.(https://t.co/Rq9DBeCMlh) Users can create new projects, create different stages for their projects. The community can submit project ideas, vote on existing ideas. Project owners pay a monthly fee per project.
3. ⛈️ Build a digital marketplace. (https://t.co/BWd1aeWMt5) Sellers can upload digital products for sale. Customers can purchase digital products and securely download. Sellers are paid out at the end of every month. Don't make it complicated, implement a great design.
4. 👨🏭 Build a job board software (https://t.co/EjWoMyqi9H). Companies can post jobs for a price, providing a link to the job application form. Jobs can be highlighted as urgent for an additional price.
