Synthetic Data Supercharges Science through AI: https://
seas.upenn.edu/stories/ai-vis
ion-reinvented-the-power-of-synthetic-data/
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Book: "Synthetic Data for Machine Learning" at http://
amzn.to/42g5wmM v/ @PacktDataML โ
๐๐ฎ๐ ๐๐ฎ๐ช๐ฝ๐พ๐ป๐ฎ๐ผ:
Avoid common data issues by identifying and solving them using synthetic data-based
MACHINE LEARNING
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Synthetic Data Revolutionizes AI and Machine Learning Applications
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Pretraining Vision and LLMs Foundation Models on AWS
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Pretrain Vision and LLMs (Large Language Models) in Python โ Techniques for Building & Deploying Foundation Models on AWS: http://
amzn.to/3pT9seD v/ @PacktDataML โโโโ
#AI #MachineLearning #MLOps #DataScience #ComputerVision #DataScientist #GenAI -
Algorithmic Trading with Python: Quant Methods Using Pandas and Scikit-learn
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The book โ#AlgorithmicTrading with Pythonโ discusses modern quant trading methods in Python with a heavy focus on PANDAS, Numpy, and scikit-learn. Get it at: http://
amzn.to/3uvYlKk + Get free code from the book here: https://
github.com/chrisconlan/al
gorithmic-trading-with-python
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Python Data Cleaning Code Snippets and Pandas Cookbook
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Simple Practical Data Cleaning #Python Code Snippets for messy data: http://
bit.ly/3hpthSD
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#DataCleaning Cookbook to prepare your data for analysis โ PANDAS, NumPy, Matplotlib, scikit-learn, etc.: http://
amzn.to/4c6DAWf v/ @PacktDataML โโโโโ
#DataScience #DataScientist -
Scikit-learn Cookbook: 80+ Python Machine Learning Recipes Third Edition
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Scikit-learn Cookbook โ 80+ recipes for #MachineLearning in Python with scikit-learn [3rd Edition]: http://
amzn.to/4oDGOq7 v/ @PacktDataML ๐๐ธ๐ท๐ฝ๐ฎ๐ท๐ฝ๐ผ:
Common Conventions & API Elements of Scikit-Learn
Pre-Model Workflow and Data Preprocessing
Dimensionality -
Mamba 3: State Space Models with RoPE for Improved Sequence Modeling
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mamba 3: Mamba with RoPE! "Improved Sequence Modeling using State Space Principles" They show that state space models can have both speed and performance! In this new iteration, Mamba now has better recurrence design, complex-valued state tracking, and a MIMO update. It
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Top priorities for AI improvement right now
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what do you think are the top things to improve right now?
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Mamba-3 Release: Linear Models for Hybrid Architectures
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Oh wow, Mamba-3 is here! For me, the most interesting use case of Mamba and Mamba-likes are the recent transformer attention hybrid architectures (Qwen3.5, Kimi Linear, etc.) Would be interesting to swap Gated DeltaNet with Mamba-3 (which now also has RoPE) in next gen hybrids. Albert Gu (@_albertgu) The newest model in the Mamba series is finally here ๐ Hybrid models have become increasingly popular, raising the importance of designing the next generation of linear models. We've introduced several SSM-centric ideas to significantly increase Mamba-2's modeling capabilities without compromising on speed. The resulting Mamba-3 model has noticeable performance gains over the most popular previous linear models (such as Mamba-2 and Gated DeltaNet) at all sizes. This is the first Mamba that was student led: all credit to @aakash_lahoti @kevinyli_ @_berlinchen @caitWW9, and of course @tri_dao! โ https://nitter.net/_albertgu/status/2033948415139451045#m
โ View original post on X โ @rasbt, 2026-03-18 02:05 UTC
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HorizonMath: Measuring AI Progress in Mathematical Discovery
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can AI do real math research? "HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification" This paper turns a vague debate into a measurable test by introducing a contamination-resistant benchmark of 101 mostly unsolved problems across 8
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AI Systems Cannot Force Self-Reflection or Introspection
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you can lead a horse to water but you can't make them introspect i'll see myself out