Microsoft literally dropped the best free Generative AI course you’ll ever see

Most classical ML books only talk about learning parameters. They lack > inference algorithms (MC-MC, EM) > Neural Networks as probabilistic function approximations > Bayesian DL, VAEs > ML models in PGM terms Kevin Murphy’s Probabilistic ML bridges these gaps. It talks about

1/11 Everyone thinks you need to spend millions on retraining to make AI smarter. What if the genius-level reasoning was already there, just hidden? A new paper (from Harvard) I just read suggests we've been looking in the wrong place. And the solution is wild. 🤯 🧵👇 2/11

You only need 20 videos

The University of Michigan dropped a full Deep Learning for Computer Vision lecture series—22 videos, no paywall. > neural nets & backprop explained from scratch > CNNs, RNNs, attention, and generative models > even detection, segmentation, and reinforcement learning

- LR is mostly about L1 (lasso) or L2 (ridge) penalty - Naive Bayes is alpha - Decision tree nobody uses as a standalone algorithm but needs to be learnt how it works - Random forest is all about max depth, no. of estimators, max features (cannot be all of them), min. samples

7 Styles of Thinking Activate your full brainpower

Kernel PCA (kPCA) is an advanced machine learning technique for non-linear dimensionality reduction. It uses the "kernel trick" to map data into a high-dimensional feature space where complex, non-linear patterns become linear. Standard PCA is then applied in this new space. This

🔒 Custom LLM Integration A production-ready solution that seamlessly integrates private LLM APIs into LangChain and LangGraph 1.0.0+ applications. Features authentication flows, logging, tool integration, and state management. 👉 Explore the full implementation:

Tutorials on Statistical Tests of Hypothesis: ANOVA; Chi Square; F-Test; Granger Causality; Likelihood-Ratio; Log Rank; Welch; Z-Test; +more... bit.ly/2EoA9NJ ———— #DataScience #Statistics #DataLiteracy #StatisticalLiteracy ——— ➕See this book: amzn.to/2W2EcpD

most books teach algorithms. this one teaches how to think about them. > this book sharpens your reasoning, not just your syntax; > perfect for students, interview preppers, and anyone who wants to solve problems like a computer scientist

6 Python Books You Can Download for FREE! clcoding.com/2025/10/6-pyth…

又一个 DeepSeek-OCR 客户端！全 Rust 实现！ 这个兼容性写得非常好，windows/linux/mac 下都能运行，而且还支持CPU或者GPU推理。并且，推理引擎也是用 Rust 写的！ 程序会自动下载模型，内置双源（Hugging Face + ModelScope）模型下载，所以不出境游也能用。 地址：github.com/TimmyOVO/deeps…

"Understanding Linear Algebra" Both the textbook and the activity workbook are available FREE. A MUST before you start AI & ML.

史上最全搞钱书籍，看完一半，年入百万。 【史上最全搞钱书籍】 立即保存：pan.quark.cn/s/58be1d4003b8

这个 AI 炒股界面做的真酷，连 AI 的投资逻辑都写上了。 来围观一下-->/rockalpha.rockflow.ai

How to use AI to extract everything from a book

Stanford's "Convex Optimization" Videos & Lecture Notes & more Course 1: see.stanford.edu/Course/EE364A Course 2: see.stanford.edu/Course/EE364B

Kolmogorov Arnold Attention is all one needs 🧠🔍 KArAt: Learnable (and more explainable) Attention This paper swaps the fixed softmax for Kolmogorov-Arnold Attention (KArAt)—a learnable operator. Why care: • Clearer attention maps: heads often lock onto whole objects, aiding