Bio-PrecisionAI Health

12/22/2025

12/22/2025

Courtesy: Marketstatics

12/15/2025

Featured in the UF Biomedical Engineering CrossLink 2025. Congratulations to Joseph Luper Tsenum for taking AI-driven drug discovery to the global stage!

https://issuu.com/ufbme/docs/crosslink_2025_db75e241a35419

Prize

11/28/2025

These 16 GitHub repos are better than any $1,000 AI/ML course:

1:// Machine Learning for beginners by Microsoft (78.5k ⭐)
https://lnkd.in/gggwDP9h

2:// 100 days of ML coding (48.6k ⭐)
https://lnkd.in/gyBQF3dv

3:// All algorithms implemented in Python (212k ⭐)
https://lnkd.in/gdeUgjsi

4:// Mathematics for Machine Learning (14.7k ⭐)
https://lnkd.in/g_TYTu5J

5:// Made with ML (44.1k ⭐)
https://lnkd.in/gMvyzFgK

6:// 60+ implementations of Deep Learning papers (64.1k ⭐)
https://lnkd.in/gR4aC2GQ

7:// Neural Networks: Zero to Hero (18.3k ⭐)
https://lnkd.in/gnusqKFa

8:// Hands-On LLMs book (17.3k ⭐)
https://lnkd.in/gT3diSRV

9:// Prompt Engineering guide (65.9k ⭐)
https://lnkd.in/gmYzhDY7

10:// AI Agents for Beginners by Microsoft (43.8k ⭐)
https://lnkd.in/ghGHGiMk

11:// Generative AI Agent techniques (17.5k ⭐)
https://lnkd.in/gq-c7URx

12:// RAG techniques (22.7k ⭐)
https://lnkd.in/g5j3ksRA

13:// Data Science to learn and apply for real world problems (27.7k ⭐)
https://lnkd.in/gFXr4msv

14:// Awesome Natural Language Processing (17.9k ⭐)
https://lnkd.in/gW9jBJcM

15:// Awesome Reinforcement Learning (9.4k ⭐)
https://lnkd.in/gyXtXQhc

16:// All Reinforcement Learning algorithms from scratch (1.2k ⭐)
https://lnkd.in/g8SdWKJU

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11/28/2025

If you want to grow in AI/ML, these are the top 16 blogs worth reading in 2025 👇

These authors train frontier models, deploy AI at scale, and write about what actually works in production:

1. Andrej Karpathy
Deep dives into neural networks, LLMs, and AI fundamentals.
🔗 https://lnkd.in/g6ZgzM7R

2. Chip Huyen
MLOps and production ML systems from someone who's built them at scale.
🔗 https://lnkd.in/gy2HHDgD

3. Sebastian Raschka, PhD
In-depth tutorials on deep learning, LLMs, and ML fundamentals with code.
🔗 https://lnkd.in/gpxCAbnm

4. Interconnects by Nathan Lambert
Analysis of AI policy, RLHF, and the business side of foundation models.
🔗 https://lnkd.in/g_vAQQTb

5. Machine Learning Mastery by Jason Brownlee
Practical tutorials on ML, deep learning, and practical implementation guides.
🔗 https://lnkd.in/gvb5ZR4E

6. Lil’Log by Lilian Weng (ex VP of Research at OpenAI)
Technical breakdowns of RL, transformers, and diffusion models.
🔗 https://lnkd.in/g7Pgwsi9

7. Eugene Yan (Principal Applied Scientist at Amazon)
Applied ML, recommender systems, and data science patterns used in production.
🔗 https://lnkd.in/gNz_wn_B

8. Philipp Schmid (Senior AI Relation Engineer at Google DeepMind)
Guides for deploying LLMs and building AI apps on cloud infrastructure.
🔗 https://lnkd.in/grVwHUMk

9. Hamel Husain (ex GitHub Staff ML Engineer)
MLOps best practices, fine-tuning workflows, and lessons from shipping ML products.
🔗 https://lnkd.in/g34R2tKC

10. Jason Liu
Structured outputs, AI agents, and production-ready LLM patterns.
🔗 https://lnkd.in/gZRYBGYp

11. Berkeley Artificial Intelligence Research Blog
Academic research on RL, robotics, computer vision, and NLP.
🔗 https://lnkd.in/gtaqkPTK

12. Hugging Face
Product updates, tutorials, and the latest from open-source AI.
🔗 https://lnkd.in/g5fJTkRT

13. Google DeepMind
Google's premier AI research division.
🔗 https://lnkd.in/gfwDCfrF

14. OpenAI Research
Latest papers on GPT models, RLHF, safety, and frontier AI systems.
🔗 https://lnkd.in/gG3bWnqY

15. Anthropic Research
Constitutional AI, interpretability, and safety research behind Claude.
🔗 https://lnkd.in/gQ8TATwi

16. Cohere Research
Enterprise LLMs, RAG systems, and production AI deployment.
🔗 https://lnkd.in/ge6wBHdq

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11/27/2025

Top 10 Generative AI concepts every AI/ML engineer should know in 2025 👇

(1) Tokenization: https://lnkd.in/gcRG4K_c
Break text into tokens (words, subwords, or bytes) for model input. Tokenization affects context length, multilingual support, and efficiency.

(2) Embeddings: https://lnkd.in/g4gqjKNH
Transforming raw data into dense vectors underpins search, clustering, semantic understanding, and RAG. Embedding space behavior is fundamental to how models “understand” inputs.

(3) Multimodal LLMs: https://lnkd.in/gX4EKtgN
Map different data types (text, images, audio, video) into a shared representation space. Allow models to reason across modalities and align concepts visually and linguistically.

(4) Retrieval-Augmented Generation (RAG): https://lnkd.in/g5tWVhwz
A hybrid approach that combines external search with LLM reasoning. RAG improves factual accuracy, reduces hallucinations, and is a popular strategy in enterprise LLM applications.

(5) Efficient Fine-Tuning (LoRA, QLoRA, PEFT): https://lnkd.in/gyug2CPF
These methods allow parameter-efficient adaptation of large models, reducing compute costs and speeding up deployment. Especially relevant for domain-specific applications.

(6) Model Context Protocol (MCP): https://lnkd.in/gXbMNuz2
Connect models to external tools, data sources, and environments through a standardized protocol. Let LLMs interact with APIs, databases, and applications safely and reliably.

(7) Model Distillation: https://lnkd.in/gWCgqBYK
A technique to compress large models into smaller ones with similar performance. Useful for edge deployment or latency-sensitive applications.

(8) Model Merging: https://lnkd.in/grxybkpm
Combining weights from multiple fine-tuned models can lead to better performance or generalization. An emerging practice for customizing foundation models.

(9) Hallucination Mitigation: https://lnkd.in/gbVKknSY
Reduce fabricated or incorrect answers by adding retrieval, constraints, or validation. Essential for production systems that require factual or domain-safe outputs.

(10) Latent Space: https://lnkd.in/gsU72Y87
A compressed, abstract representation of data where patterns emerge. Navigating this space enables generation, interpolation and creativity in AI.

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