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
Amazing resources to start learning MLOps, one of the most exciting areas in machine learning engineering:
↓
📘 Introducing MLOps
An excellent primer to MLOps and how to scale machine learning in the enterprise.
https://t.co/GCnbZZaQEI
🎓 Machine Learning Engineering for Production (MLOps) Specialization
A new specialization by https://t.co/mEjqoGrnTW on machine learning engineering for production (MLOPs).
https://t.co/MAaiRlRRE7
⚙️ MLOps Tooling Landscape
A great blog post by Chip Huyen summarizing all the latest technologies/tools used in MLOps.
https://t.co/hsDH8DVloH
🎓 MLOps Course by Goku Mohandas
A series of lessons teaching how to apply machine learning to build production-grade products.
https://t.co/RrV3GNNsLW
↓
📘 Introducing MLOps
An excellent primer to MLOps and how to scale machine learning in the enterprise.
https://t.co/GCnbZZaQEI
🎓 Machine Learning Engineering for Production (MLOps) Specialization
A new specialization by https://t.co/mEjqoGrnTW on machine learning engineering for production (MLOPs).
https://t.co/MAaiRlRRE7
⚙️ MLOps Tooling Landscape
A great blog post by Chip Huyen summarizing all the latest technologies/tools used in MLOps.
https://t.co/hsDH8DVloH
🎓 MLOps Course by Goku Mohandas
A series of lessons teaching how to apply machine learning to build production-grade products.
https://t.co/RrV3GNNsLW
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
Time for some thoughts on schools given the revised SickKids document and the fact that ON decided to leave most schools closed. ON is not the only jurisdiction to do so, but important to note that many jurisdictions would not have done so -even with higher incidence rates.
As outlined in the tweet by @NishaOttawa yesterday, the situation is complex, and not a simple right or wrong https://t.co/DO0v3j9wzr. And no one needs to list all the potential risks and downsides of prolonged school closures.
On the other hand: while school closures do not directly protect our most vulnerable in long-term care at all, one cannot deny that any factor potentially increasing community transmission may have an indirect effect on the risk to these institutions, and on healthcare.
The question is: to what extend do schools contribute to transmission, and how to balance this against the risk of prolonged school closures. The leaked data from yesterday shows a mixed picture -schools are neither unicorns (ie COVID free) nor infernos.
Assuming this data is largely correct -while waiting for an official publication of the data, it shows first and foremost the known high case numbers at Thorncliff, while other schools had been doing very well -are safe- reiterating the impact of socioeconomics on the COVID risk.
A group of Ontario experts led by SickKids has updated its guidance for school operation during the COVID-19 pandemic. The living document, COVID-19: Updated Guidance for School Operation During the Pandemic, can be read here: https://t.co/rotLqDqkQh pic.twitter.com/q7kVezAPoG
— SickKids_TheHospital (@SickKidsNews) January 21, 2021
As outlined in the tweet by @NishaOttawa yesterday, the situation is complex, and not a simple right or wrong https://t.co/DO0v3j9wzr. And no one needs to list all the potential risks and downsides of prolonged school closures.
1/It's the eve of provincial announcements on schools reopening for in-person instruction.
— Nisha Thampi (@NishaOttawa) January 20, 2021
Households are under stress and experts are divided on whether schools are unicorns or infernos.
Everyone wants to do right by kids, who have borne so much throughout this pandemic.
On the other hand: while school closures do not directly protect our most vulnerable in long-term care at all, one cannot deny that any factor potentially increasing community transmission may have an indirect effect on the risk to these institutions, and on healthcare.
The question is: to what extend do schools contribute to transmission, and how to balance this against the risk of prolonged school closures. The leaked data from yesterday shows a mixed picture -schools are neither unicorns (ie COVID free) nor infernos.
Assuming this data is largely correct -while waiting for an official publication of the data, it shows first and foremost the known high case numbers at Thorncliff, while other schools had been doing very well -are safe- reiterating the impact of socioeconomics on the COVID risk.
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One of the most successful stock trader with special focus on cash stocks and who has a very creative mind to look out for opportunities in dark times
Covering one of the most unique set ups: Extended moves & Reversal plays
Time for a 🧵 to learn the above from @iManasArora
What qualifies for an extended move?
30-40% move in just 5-6 days is one example of extended move
How Manas used this info to book
Post that the plight of the
Example 2: Booking profits when the stock is extended from 10WMA
10WMA =
Another hack to identify extended move in a stock:
Too many green days!
Read
Covering one of the most unique set ups: Extended moves & Reversal plays
Time for a 🧵 to learn the above from @iManasArora
What qualifies for an extended move?
30-40% move in just 5-6 days is one example of extended move
How Manas used this info to book
The stock exploded & went up as much as 63% from my price.
— Manas Arora (@iManasArora) June 22, 2020
Closed my position entirely today!#BroTip pic.twitter.com/CRbQh3kvMM
Post that the plight of the
What an extended (away from averages) move looks like!!
— Manas Arora (@iManasArora) June 24, 2020
If you don't learn to sell into strength, be ready to give away the majority of your gains.#GLENMARK pic.twitter.com/5DsRTUaGO2
Example 2: Booking profits when the stock is extended from 10WMA
10WMA =
#HIKAL
— Manas Arora (@iManasArora) July 2, 2021
Closed remaining at 560
Reason: It is 40+% from 10wma. Super extended
Total revenue: 11R * 0.25 (size) = 2.75% on portfolio
Trade closed pic.twitter.com/YDDvhz8swT
Another hack to identify extended move in a stock:
Too many green days!
Read
When you see 15 green weeks in a row, that's the end of the move. *Extended*
— Manas Arora (@iManasArora) August 26, 2019
Simple price action analysis.#Seamecltd https://t.co/gR9xzgeb9K
1/ Here’s a list of conversational frameworks I’ve picked up that have been helpful.
Please add your own.
2/ The Magic Question: "What would need to be true for you
3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.
“Gun to the head—what would you decide now?”
“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”
4/ Other Q’s re: decisions:
“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”
“What’s end-game here?”
“What does success look like in a world where you pick that path?”
5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:
“What would the best version of yourself do”?
Please add your own.
2/ The Magic Question: "What would need to be true for you
1/\u201cWhat would need to be true for you to\u2026.X\u201d
— Erik Torenberg (@eriktorenberg) December 4, 2018
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody: https://t.co/Yo6jHbSit9
3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.
“Gun to the head—what would you decide now?”
“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”
4/ Other Q’s re: decisions:
“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”
“What’s end-game here?”
“What does success look like in a world where you pick that path?”
5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:
“What would the best version of yourself do”?
