TowerQFBM

help me

[TowerQFBM @QfbmTower]

@manishamishra24 All

[TowerQFBM @QfbmTower]

@manishamishra24 Need

[みやさかしんや@Python/AI/DX @miyashin_prg]

約2,500万件のデータセットにアクセス可能な検索サイトをGoogleが提供🤗✨機械学習だけでなく、仕事の仮説検証、学生の卒業論文など幅広く活用できて嬉しい🤗✨

https://t.co/ZCBtXeSuCs

[NMO @NMaols]

Cuál es el nuevo scihub y el nuevo annas archive? Ayuda

[Stephen Turner 🦋 @stephenturner.us @strnr]

poisonfrogs: Color Palettes Inspired by Neotropical Poison Frogs https://t.co/YaLAIsW4oU #Rstats

[Karan🧋 @kmeanskaran]

Project expectations for ML position:

2020: Linear regression on house prices
2021: ANN on customer churn
2022: VGG16 on X-ray images
2023: Text classification using BERT
2024: Fine-tune LLM using Llama
2025: AI agents for emails
2026: Deploy LLMs using Ops

[Alex Prompter @alex_prompter]

🚨 MIT proved you can delete 90% of a neural network without losing accuracy.

Five years later, nobody implements it.

"The Lottery Ticket Hypothesis" just went from academic curiosity to production necessity, and it's about to 10x your inference costs.

Here's what changed (and why this matters now):

[TowerQFBM @QfbmTower]

@Ai_Vaidehi ML

[Nick Desnoyer @NickDesnoyer]

After 10 Fridays, 10 flowers, and 100+ drawings, the Florédex is complete! ✨

Come take a tour of the design and science behind 10 iconic flowers 🌺🧵

[Cole DeForest @coledeforest]

Check out our new paper entitled "Boolean Logic-based Controlled Release of Bioactive Proteins with Diversified Inputs", live in @angew_chem!

Growth factors, enzymes, nanobodies, cytokines, and fluorescent proteins can now be "logically" delivered!

📜: https://t.co/0ECwq8V8mR

[TowerQFBM @QfbmTower]

@DAIEvolutionHub Thank you bro

[TowerQFBM @QfbmTower]

@DAIEvolutionHub AI

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

Dear Future AI Engineer,

If you want to break into AI in 2025, stop chasing trends. Start mastering the fundamentals.

I’m giving away 2 must-read O’Reilly books 📚 that every AI Engineer swears by — from Deep Learning to NLP with Transformers.

These books will change your entire AI foundation 👇
• Fundamentals of Deep Learning
• Natural Language Processing with Transformers

Ready to level up? Here’s how ⬇️
1️⃣ Follow me (so I can DM you)
2️⃣ Like & Repost
3️⃣ Comment “AI”

💡 Knowledge builds empires. Start building yours today.

[TowerQFBM @QfbmTower]

@Ai_Vaidehi ML

[ℏεsam @Hesamation]

Harvard just dropped a book on ~ML systems engineering~ and it's 100% free in PDF (it will be published in print by MIT Press).

systems engineering is the hot skill companies die to see in candidates, but few books explain it in the context of ML.

What it covers:
→ ML system foundations
→ a primer on DL
→ DNN architectures
⃗→ design principles and AI workflows
⃗→ data engineering
→ AI training (distributed, mathematics, etc.)
⃗→ performance engineering and model optimization
⃗→ AGI systems and what to expect!!!
⃗→ how to work with SLMs and VLMs

the subjects cover 100% of what you need to know as a mid-level ML engineer, and the best part is that it is updated to cover what matters in 2025.

get the book here: https://t.co/YahoOBRyj5

[TowerQFBM @QfbmTower]

@DAIEvolutionHub Send

[Kshitij Mishra | AI & Tech @DAIEvolutionHub]

𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)

1. Artificial Intelligence + Data Analyst
2. Machine Learning + Data Science
3. Cloud Computing + Web Development
4. Ethical Hacking + Hacking
5. Data Analytics + DSA
6. AWS Certified + IBM COURSE
7. Data Science + Deep Learning
8. BIG DATA + SQL COMPLETE COURSE
9. Python + OTHERS
10 MBA + HANDWRITTEN NOTES

(72 Hours only ) Cost About - $500

To get FREE: -
1. Follow (So I can DM you)
2. Like & retweet
3. Reply " Send "

[Manolis Kellis @manoliskellis]

MIT Course announcement: Machine Learning for Computational Biology #MLCB25
Fall'24 Lecture Videos: https://t.co/tA3zeuIF7g
Fall'24 Lecture Notes: https://t.co/C3WmXZuQur
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery. 
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis @manoliskellis, Prof. Eric Alm @ejalm, TAs: Ananth Shyamal, Shitong Luo @luost26
Course website: https://t.co/ateGr6xKLM
@MIT @MITEECS @MITdeptofBE @MITCSBPhD @MIT_CSAIL @Harvard @HarvardMed @BroadInstitute

[Python Coding @clcoding]

📚 Weekend Giveaway – 3 Hard Copies!

Win a copy of “Building Neo4j-Powered Applications with LLMs” – perfect for anyone exploring LLM-driven search & recommendations with Neo4j, Haystack, LangChain4j, and Spring AI.

How to enter:

1️⃣ Retweet this post
2️⃣ Like this post
3️⃣ Comment “Neo4j” below

🔗 Must read Book Review: https://t.co/6LA4uLVxSw

[Matt Dancho (Business Science) @mdancho84]

Stop doing Customer Segmentation with plain vanilla Scikit Learn.

Add these 7 Python libraries to your RFM, clustering, and
customer segmentation projects: