Machine Learning
1297 Threads
Machine Learning Roadmap for BEGINNERS with resources!! π€π§΅ 1. Study mathematical concepts:- a) Linear Algebra b) Calculus c) Probability d) Statistics 2. Pick your programming l...
Why does every beginner data scientist fall for the **deep learning trap**? When I was first learning data science this cost me at least 6-months. Seriously... An #rstats #pytho...
βExploring Plain Vision Transformer Backbones for Object Detectionβ https://t.co/E1POjnFmgZ Excellent read as usual from the FAIR team. Strong object detection results with only mi...
Error analysis, 101: Time-series data The BEST way to improve ML models is with error analysis -- seeing how your model screws up. The problem? Error analysis is really hard, an...
The best way to 10x your data science skills is by building. Building is irreplaceable. Courses/bootcamps can never teach you what building does. The problem? It's only useful i...
What is Interpretable Machine Learning? A lot of things have been said about IML because of the increasing need for the interpretation of complicated models. What is it exactly an...
Universities do a terrible job teaching machine learning. Not only do they give you critically out-of-date information, but they focus most of their time on the least important as...
Machine Learning Explained π¨βπ« PCA Principal Component Analysis is a commonly used method for dimensionality reduction. It's a good example of how fairly complex math can have a...
Make it simple & stick to your head forever. Data science = Extract insight & make Decision with Data Data Science is not Machine Learning, It uses Machine Learning approach to s...
What are some good papers/tech blogs sharing how bandits are used in recommendation systems?
Machine Learning Formulas Explained π¨βπ« For regression problems you can use one of several loss functions: βͺοΈ MSE βͺοΈ MAE βͺοΈ Huber loss But which one is best? When should you pref...
Compilation thread of various Keras tips β¬οΈβ¬οΈβ¬οΈ
I'll be highlighting some of my favorite technical books related to machine learning and applied math in a sequence of tweets. So stay tuned.
Seeing all these threads about habits of good ML modelers on this app but many of them go out the window when it comes to prod ML. For instance, it's possible to have "too much" ex...
RL agents can make decisions in 2 basic ways: 1) model-free 2) model-based Let's discuss model-based RL methods. - How do they work? - Categories of model-based RL - Advantages +...
Machine Learning Formulas Explained! π¨βπ« This is the formula for the Binary Cross Entropy Loss. It is commonly used for binary classification problems. It may look super confusin...
Retrieval-based models are increasingly important in NLP/QA. But an important factor in modeling text is knowing *where* it came from. Our #ICLR2022 paper proposes retrieval-based...
How to efficiently read and understand machine learning research papers:
[π₯³ learn ML! π₯³] The 8th iteration of the summer school of Machine Learning is happening again this year in Belgrade, Serbia! This is how I got started with ML! β€οΈ- so needless to s...
Curious about recommender models? Interested in endowing models from other domains with some of their superpowers? Please join me on a whirlwind tour of 6 recsys architectures!...
I love this site. I will randomly be scrolling through my feed and see something insanely cool like this, watch the video (https://t.co/De3YJlPvUP), and get very excited because ML...
Here are the list of courses i took when i started learning data science and machine learning last year 1. Complete data science and machine learning bootcamp from Zero to mastery...
@nattyice @drewbanin All of what you say makes a ton of sense. But think the one size fits all DB is an ideal -- in my (limited) experience, the new workload precedes the DB custom...
Five very helpful Open Source Projects you should give a star on Github π - Backend Development - Artificial Intelligence - Python - Machine Learning - Cryptocurrency Thread π§΅
