Linear & Logistic Regression,
Naive Bayes, SVMs, Kernels
Decision Trees, Introduction to Neural Networks
Debugging ML Models.
https://t.co/cMLzvsdIcT
A lot of Machine Learning (ML) I learned during my Ph.D. was from youtube. I didn't have a guide to do this effectively and thus here it is:
A complete guide to studying ML from youtube: 13 best and most recent ML courses available on YouTube. 👩🏫🧵⤵️
Linear & Logistic Regression,
Naive Bayes, SVMs, Kernels
Decision Trees, Introduction to Neural Networks
Debugging ML Models.
https://t.co/cMLzvsdIcT
Explainability in AI, Precession vs. Recall, Statistical Significance, Clustering and K-means, and finally, Ensemble models. https://t.co/LiujYMWFbT
The foundations of deep learning, how to build different neural networks (CNNs, RNNs, LSTMs, etc...), how to lead machine learning projects and finally - AI and Healthcare. https://t.co/F1jBHejS5k
Optimization and Calculus, Overfitting and Underfitting, Regularization, Monte Carlo Estimation, and Maximum Likelihood Learning. https://t.co/2znEMgrJvf
Build & deploy deep learning models for vision & NLP. Use PyTorch, plus popular libraries like fastai.
https://t.co/xTg00k7wrt
PageRank, Matrix Factorizing, Node Embeddings, Graph Neural Networks, Knowledge Graphs, and finally, Deep Generative Models for Graphs.
https://t.co/hkgfoFoB9O
Reasoning about uncertainty, Continuous Variables, Sampling, and Markov Chain Monte Carlo. https://t.co/Z76gVxeI3d
Autoregressive Models, Latent Variable Models, & Self-supervised learning.
https://t.co/ywkSKC5r5w
Transformers, its applications in Language (GPT-3), vision & universal compute
engines. https://t.co/nkTtSCG854
I tweet resources for big data research in healthcare. Follow me @Sanjusinha7 if that is of interest. See below for other such resources.
Methods & data available to you are your thinking tools. While I learned the methods in my classes, I wish I knew various data available to me.
— Sanju Sinha (@Sanjusinha7) September 16, 2022
10 resources to learn almost all the big data resources available in cancer research. \U0001f9f5\U0001f447
https://t.co/1kqZ03zmJd
I curated a list of 28 common issues one faces while using machine learning for biomedicine research and using different kinds of omics data. I also provided guides on how to best overcome them. \U0001f349\U0001f9f5\U0001f447 pic.twitter.com/TFhwTOZsij
— Sanju Sinha (@Sanjusinha7) November 2, 2022
https://t.co/drV8IQSDJY
Our understanding of the immune system is quickly growing.
— Sanju Sinha (@Sanjusinha7) October 1, 2022
11 resources (videos and papers) covering the fundamentals and computational tools available to study the immune system. \U0001f9f5\U0001f447\U0001f52c\U0001f912
Best computational practices to analyze Spatial Transcriptomics (ST) are yet non-trivial.
— Sanju Sinha (@Sanjusinha7) October 7, 2022
14 Resources, including videos, papers, data repo, tutorial, & a podcast, covering our current understanding of preprocessing & downstream analysis of Spatial Transcriptomics. \U0001f30c\U0001f9ec \U0001f9f5\U0001f447
https://t.co/U1NbzVZMlN
Using big data in healthcare. Here are 10 educational resources for anyone interested in building skills to analyze big data in healthcare.
— Sanju Sinha (@Sanjusinha7) October 14, 2022
Ranging from introductory to advanced, this includes courses, youtube channels, papers & online books.\U0001f9f5\U0001f951\U0001f447
https://t.co/IUqnLLlmuW
Interested in aging and cancer. I did a year of literature survey on this.
— Sanju Sinha (@Sanjusinha7) September 29, 2022
Here is my list of 20 key open questions and challenges to better understand the interplay between aging and cancer. A thread \U0001f9f5\U0001f447 pic.twitter.com/gd6NX1VJM7
I would love to chat if you would like to collaborate on this or a potential investor. DM/email [email protected]
https://t.co/ketww3SwSY
Drug target identification is at the heart of drug development, and we\u2019ve been working to change how it\u2019s been done.
— Eytan Ruppin, MD, PhD (@NCIEytanRuppin) October 20, 2022
We present DeepTarget: a new computational tool to characterize a drug\u2019s mechanism of action in-depth beyond its primary target. \U0001f9f0\U0001f9f5\U0001f447
https://t.co/MuNTjsiniI pic.twitter.com/8g20uUotxp
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MASTER THREAD on Short Strangles.
Curated the best tweets from the best traders who are exceptional at managing strangles.
• Positional Strangles
• Intraday Strangles
• Position Sizing
• How to do Adjustments
• Plenty of Examples
• When to avoid
• Exit Criteria
How to sell Strangles in weekly expiry as explained by boss himself. @Mitesh_Engr
• When to sell
• How to do Adjustments
• Exit
Beautiful explanation on positional option selling by @Mitesh_Engr
Sir on how to sell low premium strangles yourself without paying anyone. This is a free mini course in
1st Live example of managing a strangle by Mitesh Sir. @Mitesh_Engr
• Sold Strangles 20% cap used
• Added 20% cap more when in profit
• Booked profitable leg and rolled up
• Kept rolling up profitable leg
• Booked loss in calls
• Sold only
2nd example by @Mitesh_Engr Sir on converting a directional trade into strangles. Option Sellers can use this for consistent profit.
• Identified a reversal and sold puts
• Puts decayed a lot
• When achieved 2% profit through puts then sold
Curated the best tweets from the best traders who are exceptional at managing strangles.
• Positional Strangles
• Intraday Strangles
• Position Sizing
• How to do Adjustments
• Plenty of Examples
• When to avoid
• Exit Criteria
How to sell Strangles in weekly expiry as explained by boss himself. @Mitesh_Engr
• When to sell
• How to do Adjustments
• Exit
1. Let's start option selling learning.
— Mitesh Patel (@Mitesh_Engr) February 10, 2019
Strangle selling. ( I am doing mostly in weekly Bank Nifty)
When to sell? When VIX is below 15
Assume spot is at 27500
Sell 27100 PE & 27900 CE
say premium for both 50-50
If bank nifty will move in narrow range u will get profit from both.
Beautiful explanation on positional option selling by @Mitesh_Engr
Sir on how to sell low premium strangles yourself without paying anyone. This is a free mini course in
Few are selling 20-25 Rs positional option selling course.
— Mitesh Patel (@Mitesh_Engr) November 3, 2019
Nothing big deal in that.
For selling weekly option just identify last week low and high.
Now from that low and high keep 1-1.5% distance from strike.
And sell option on both side.
1/n
1st Live example of managing a strangle by Mitesh Sir. @Mitesh_Engr
• Sold Strangles 20% cap used
• Added 20% cap more when in profit
• Booked profitable leg and rolled up
• Kept rolling up profitable leg
• Booked loss in calls
• Sold only
Sold 29200 put and 30500 call
— Mitesh Patel (@Mitesh_Engr) April 12, 2019
Used 20% capital@44 each
2nd example by @Mitesh_Engr Sir on converting a directional trade into strangles. Option Sellers can use this for consistent profit.
• Identified a reversal and sold puts
• Puts decayed a lot
• When achieved 2% profit through puts then sold
Already giving more than 2% return in a week. Now I will prefer to sell 32500 call at 74 to make it strangle in equal ratio.
— Mitesh Patel (@Mitesh_Engr) February 7, 2020
To all. This is free learning for you. How to play option to make consistent return.
Stay tuned and learn it here free of cost. https://t.co/7J7LC86oW0
1/ Some initial thoughts on personal moats:
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
I'm increasingly interested in the idea of "personal moats" in the context of careers.
— Erik Torenberg (@eriktorenberg) November 22, 2018
Moats should be:
- Hard to learn and hard to do (but perhaps easier for you)
- Skills that are rare and valuable
- Legible
- Compounding over time
- Unique to your own talents & interests https://t.co/bB3k1YcH5b
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
People talk about \u201cpassive income\u201d a lot but not about \u201cpassive social capital\u201d or \u201cpassive networking\u201d or \u201cpassive knowledge gaining\u201d but that\u2019s what you can architect if you have a thing and it grows over time without intensive constant effort to sustain it
— Andrew Chen (@andrewchen) November 22, 2018
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
Things that look like moats but likely aren\u2019t or may fade:
— Erik Torenberg (@eriktorenberg) November 22, 2018
- Proprietary networks
- Being something other than one of the best at any tournament style-game
- Many "awards"
- Twitter followers or general reach without "respect"
- Anything that depends on information asymmetry https://t.co/abjxesVIh9
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
