Henry Njoku

Researcher || Bioinformatics. Editor @JXBot

[Mushtaq Bilal, PhD @MushtaqBilalPhD]

Here's a Zotero+Research Rabbit workflow that you can master in 25min to make your literature review process super-smooth:

[Caglar @caglar_ee]

Video lectures, GaTech CS 4803 / 7643 Deep Learning fall 2020, by Dhruv Batra
https://t.co/na3AXLuOjl
.

[African BioGenome Project @DAISEA_AfricaBP]

To register, please visit: https://t.co/mkulC4qmrm

@CraigKinnear2
@MRCza
@nanopore

[Pooja Pawar, PhD @SheExploresData]

Python string methods look simple, but they power real data cleaning, validation, and text processing. lower, split, replace, find, and join are used daily in analytics and automation workflows. Mastering them saves hours of manual effort. #Python #DataAnalysis #Programming #Automation

[ICTP-SAIFR @ictpsaifr]

Applications are open for the School on Analytical and Numerical Methods for Disordered Quantum Systems, focused on key topics in Beyond the Standard Model phenomenology.

[Antonio Lupetti @antoniolupetti]

"Computing Neural Network Gradients" is a clear introduction to the mathematics behind backpropagation and gradient computation in neural networks.

Published as part of Stanford CS224N, these notes walk through the chain rule, computational graphs, vectorised derivatives, and efficient gradient calculations with concrete examples rather than black-box formulas.

Gradients are at the heart of modern deep learning. Every parameter update in a neural network, from a small classifier to today's Transformer-based LLMs, ultimately relies on backpropagation and efficient gradient computation.

https://t.co/vQ9xoTDSYD

[Sam Altman @sama]

one of the quotes i find most inspiring on a hard day:

"Whatever your hand finds to do, do it with all your might, for in the realm of the dead, where you are going, there is neither working nor planning nor knowledge nor wisdom"

Ecclesiastes 9:10

[Dan Kornas @DanKornas]

🔗 GitHub: https://t.co/o9YBasKIQT
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✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd

[African Bioinformatics Institute @AfrBioInstitute]

Good experimental design is the foundation of research that holds up. ABI and the African Plant Breeders Association are hosting a free, open Agri-Informatics Lecture Series from June 2026. Register for each session separately.
https://t.co/I1Riw8rIa1 👇

[Prof. Dr. Sezgin Bakırdere @SezginBakirdere]

Genç bilim insanlarına küçük bir tavsiye…

Makalelerinize uygun dergi bulmakta zorlanıyorsanız aşağıdaki araçları kullanmanızı öneririm;

1️⃣Springer Journal Suggester
https://t.co/S8BU2Zam1Z

2️⃣Elsevier Journal Finder
https://t.co/bgGUGeK72u

3️⃣Match my manuscpt (Web of Science)
https://t.co/TRuoYxmCrs

Makalenizin başlığını, özetini ve anahtar kelimelerini girerek uygun dergi önerileri alabilirsiniz🌺

[Python Coding @clcoding]

Mastering Python for Artificial Intelligence: Learn the Essential Coding Skills to Build Advanced AI Applications (Free PDF)

Download Free Book: https://t.co/Qc3mADKhSy

[Python Programming @PythonPr]

Machine Learning Time Complexity

[Dan Kornas @DanKornas]

🔗 GitHub: https://t.co/HSWSPs0KJQ
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✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd

[Avinash Singh @AvinashSingh_20]

CS Fundamentals Drive Resource → https://t.co/IisKTZsToT

[Dan Kornas @DanKornas]

🔗 GitHub: https://t.co/Lm0Ly7dIPX
---
✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): https://t.co/zJ9uwd6qSd

[Turing Post @TheTuringPost]

A must-read survey to refresh math and gen AI basics → The Little Book of Generative AI Foundations: An Intuitive Mathematical Primer

It shows a clear walkthrough of how gen AI learns to understand, model, and create complex data, covering:

- Latent algebra foundations: PCA, SVD, autoencoders
- Latent models: PPCA and VAEs
- VAEs: ELBO, inference, reparameterization
- Diffusion: the way from noise → denoising
- Score-based and continuous-time generative modelling
- Density models: flows, autoregression
- GANs and energy-based models beyond likelihoods

[Vaishnavi @_vmlops]

Agentic AI
- A Complete Learning Guide
for High School Students

https://t.co/QRU2VqUNFG

[Dhanian 🗯️ @e_opore]

Day 14/100 of Learning System Design.

Microservices Architecture – Pros, Cons, and Challenges.

Grab the System Design Ebook: https://t.co/1pR2xx1I3p

[Arun Chauhan @imarun_chauhan]

𝟯𝟬 𝗕𝗹𝗼𝗴𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝟯𝟬 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

1. APIs: https://t.co/7H7a1hbeTT
2. API Gateways: https://t.co/7D0lFhOkL5
3. JWTs: https://t.co/zZHHBM9wBb
4. Webhooks: https://t.co/dmNCRFSqj1
5. REST vs GraphQL: https://t.co/iy4pgOGgFc
6. Load Balancing: https://t.co/dZM2ljxWcJ
7. Proxy vs Reverse Proxy: https://t.co/AoKQi84yk0
8. Scalability: https://t.co/ZY345ro6Kt
9. Availability: https://t.co/sGMTFelPVq
10. SPOF: https://t.co/FEKM4xayDi
11. CAP Theorem: https://t.co/wihSO7yRyn
12. SQL vs NoSQL: https://t.co/QtMfhVK8N7
13. ACID Transactions: https://t.co/VZbabQ3XZ3
14. Database Indexes: https://t.co/2W2zz8ACnp
15. Database Sharding: https://t.co/B3c343tHdq
16. Consistent Hashing: https://t.co/uxnWwwQCXR
17. CDC: https://t.co/CkVR7LieSv
18. Caching: https://t.co/smvgorjEYo
19. Caching Strategies: https://t.co/ZU2Jfe8CPY
20. Cache Eviction Policies: https://t.co/TATblyQDLN
21. CDN: https://t.co/hZuGvCKZVW
22. Rate Limiting Algorithms: https://t.co/eajEzctOum
23. Message Queues: https://t.co/Pkxsxtbfev
24. Bloom Filters: https://t.co/a2gXDaLXiO
25. Idempotency: https://t.co/afRE4i5dOX
26. Concurrency vs Parallelism: https://t.co/xZs7ofHcag
27. Long Polling vs WebSockets: https://t.co/Sq04tipuMk
28. Stateful vs. Stateless Architecture: https://t.co/iIVOOm7OdG
29. Batch vs Stream Processing: https://t.co/Awyb4eK31s
30. Geohashing: https://t.co/lQPefhiWoI

🔁 Repost to help a friend.
📌 Save this post for later.

Follow @imarun_chauhan for more resources daily 🚀

[tut_ml @tut_ml]

Learn LLMs- https://t.co/zSv03eLjLI