krish krishna kashyap

[Ebokify @ebokify]

Building Machine Learning Systems with a Feature Store: Batch, Real-Time, and LLM Systems

📕 Get Now: https://t.co/fZYAv4OlI5

#MachineLearning

[Ebokify @ebokify]

GraphRAG: The Definitive Guide: Patterns and Techniques for GenAI Knowledge Graph Retrieval

📕 Get Now: https://t.co/QIlJRZ20Tp

As powerful as large language models (LLMs) have become, they often fail to deliver accurate, explainable results due to their limited access to reliable data. GraphRAG is the next evolution of the foundational retrieval-augmented generation (RAG) architecture, combining the structured intelligence of knowledge graphs with the power of #LLMs to deliver deeper, more trustworthy outputs. This book gives you the tools, skills, and confidence to make sense of this complex approach.

[Kirk Borne @KirkDBorne]

Transformers from Scratch — Beginner's Guide to the Neural Network Behind Large Language Models — Self-Attention, the Math, and Working Code in Python and PyTorch: https://t.co/3MTpnEh18N
+
Mastering Transformers: https://t.co/tWnbF4GQDF

[DeepLearning.AI @DeepLearningAI]

Make predictions on tabular data without training a new model for each dataset.

In Tabular Foundation Models for Data Science Agents, built with @prior_labs, you'll give an AI agent a tabular foundation model as a prediction tool.

Enroll for free: https://t.co/hOsNojpvnT

[Kirk Borne @KirkDBorne]

Start Doing Graph Theory — introductory textbook about using graph theory to solve puzzles, prove theorems, and everything in between. It is freely available [478-page PDF].

Download at https://t.co/mPyDrMxB7E

[Fanout @Fanoutdotsh]

infererence 10infererence 101

[Aviv Tamar @AvivTamar1]

Physical copies have arrived!

Want to learn more?
https://t.co/q0nUgDCAhj

[Kirk Borne @KirkDBorne]

Open-source PDF ebook >> “Reinforcement Learning — Mathematics and Algorithms from MDPs to RLHF, InstructGPT, and DeepSeek-R1, with Python”

Available at https://t.co/9k3Ckf6bTW

And here: https://t.co/YGCS8lkWzl

[krish krishna kashyap @krishzen]

@narendramodi @gupta_rekha or Mr mahamanav ispe bhi kuch bolie.. jaise nale se gas banane ka nuskha dia tha.. waise hi kuch dijiye Modi as mahamanav..

[AlphaSignal @AlphaSignalAI]

AlphaSignal scans arXiv, HuggingFace, GitHub, and X every day and surfaces the top 1% of AI developments that actually matter to engineers.

One daily briefing that filters 500+ sources down to the models, papers, and repos worth knowing about.

[DeepLearning.AI @DeepLearningAI]

One AI detector score cost an author a shot at France’s top literary prize.
Meanwhile, some AI leaders want frontier models locked away.

Our weekly AI newsletter’s Halloween issue on witch hunts and sealed tombs:

https://t.co/0lAJp4kVE9

#DeepLearningAI #AIEthics #ResponsibleAI

[alphaXiv @askalphaxiv]

Yann LeCun and Randall Balestriero just Proved the Platonic Representation Hypothesis, and found a better way to achieve it.

The Platonic Representation Hypothesis suggests that AI models trained on different modalities, like images and text, eventually learn similar representations of the world. But this paper shows that how models are trained matters more than simply scaling them up.

So they mathematically show that, under certain conditions, two JEPAs trained completely separately can learn essentially the same underlying representation of the world. One learns from different images of the same scene, while the other learns from different descriptions of that scene, without ever being trained on image-text pairs.

Their approach achieves the same image-text alignment with 42x fewer training samples than next-token prediction. A 109M-parameter text JEPA even outperforms LLMs up to 72B parameters on cross-modal alignment benchmarks.

https://t.co/3j1enq9qF7

[Haider. @haider1]

Yann LeCun says:

People in AI have made the same mistake for 70 years: if a machine can do a task humans see as intellectually complex, it must be smarter than humans. This is completely false

We have computers that are superhuman at many tasks but we're still far from machines with human or even animal-level intelligence

[Kirk Borne @KirkDBorne]

Reinforcement Learning — Industrial Applications of Intelligent Agents: https://t.co/1eoKUMwp5p

Practical book with these benefits:
🔴 Learn what RL is and how the algorithms help solve problems
🔴 Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning
🔴 Dive deep into a range of value and policy gradient methods
🔴 Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning
🔴 Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more
🔴 Get practical examples through the accompanying website

[DeepLearning.AI @DeepLearningAI]

Building an agent that works once is easy, but the real challenge is keeping it reliable in production. Yann LeCun, Andrew Ng + Engineers from @OpenAI, @BlackRock @huggingface, @neo4j and are teaching at AI Dev NYC on:

• Evaluation-driven development
• Building agentic systems
• Scaling and operating in production

📅Nov 30–Dec 1 · Chelsea Piers, NYC
🎟Get your tickets: https://t.co/j0p635aY0B

#AIDev #DeepLearningAI #AIEngineeringSkills

[DeepLearning.AI @DeepLearningAI]

📰From The Batch:

🔄Meta returns to open weights. Muse Spark Open is now the strongest open weights model from outside China, and it takes text, images, video, and audio as input. 🎥
It even runs on two high-memory desktops. 💻

📬 https://t.co/I7oz1dTspR

#DeepLearningAI #OpenWeights #AIEngineering

[Satya Nadella @satyanadella]

Just wrapped our earnings call. It was a fast start to our fiscal year.

Microsoft Cloud revenue surpassed $63B, with growth accelerating to 30% YoY.

Lots of innovation as we build the new tech stack for the agentic wave. A few highlights:

[DeepLearning.AI @DeepLearningAI]

🧰 Picking the most relevant tools one by one leaves gaps. HYSET scores tools as a set.

📊 Recall@5: 88.6% vs. 81.4%
✅ Tasks resolved: 69.9% vs. 61.8%

Full story in our weekly newsletter for AI news and research, The Batch 📬 https://t.co/JqWJU13cFL

#DeepLearningAI #AIAgents #AIEngineering

[Google Gemma @googlegemma]

Welcome to the new Gemma documentation!

We've built a brand new home for developers to explore, experiment, and build.

What’s new?
🌐 Global model selector for customized code snippets
🤖 Better agent support (turn any page into Markdown!)
📊 Data-driven backend for a consistent experience
⚡️ On-device demos running directly in your browser!

Check it out: https://t.co/yx9K4xGH8M

[Google DeepMind @GoogleDeepMind]

Gemini 4 Argon is already unlocking new scientific breakthroughs in quantum computing, nuclear fusion, protein design and beyond. 🧵