Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas!

It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀

Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!

🌟 hfviewer.com

#HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 A large collection of lectures on Machine Learning and Deep Learning 🧠

We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖

Excellent material for both learning and reviewing key topics. 📚

⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle

#MachineLearning #DeepLearning #AI #Tech #Coding #Learning

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers

🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.

📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.

📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI

🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.

💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.

🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium

#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

sequence of four inputs, carrying every hidden state forward yourself. 🔄

1. Given

Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊

2. Initialize

Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑

3. First hidden layer (a)

We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮

4. Second hidden layer (b)

Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️

5. Third hidden layer (c)

Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁

6. Output layer (y)

Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝

7. Carry the states forward

We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀

8. Process X2

Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢

9. Carry the states forward

Let us copy a2, b2, c2 across, exactly as before. 🔄

10. Process X3

Same four moves, third input. Y3 = [13, -1, 9]. 🧩

11. Carry the states forward

We copy a3, b3, c3 across, one last time. ⏭️

12. Process X4

Repeat once more. Y4 = [15, 7, 2]. ✅

You have just run a Deep RNN over a whole sequence by hand. ✍️

The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]

The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠

#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

I kept running into the same problem: some of the best AI/ML books are legally free. The authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.

So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.

Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄

It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝

Repo:
https://github.com/MarcosSete/awesome-free-ai-books

#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 Learning Data Science through interactive examples

One of the most useful repositories for those who want to better understand machine learning.

It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results.

⛓ Link to GitHub
https://github.com/GeostatsGuy/DataScienceInteractivePython

#DataScience #MachineLearning #Python #Learning #Tech #GitHub

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 Over 300 real-world case studies of ML systems from top companies. 🤖

We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️

⛓ Link to GitHub
https://github.com/Engineer1999/A-Curated-List-of-ML-System-Design-Case-Studies

#MachineLearning #MLCaseStudies #DataScience #Engineering #Uber #Netflix

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Attention Heatmap vs Token Pruning 🔍✂️

🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms

#AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🚀 TOP 8 Machine Learning Regression Metrics Explained

Choosing the right metric isn't academic; it's the difference between a model that works in production and one that breaks trust.

Here's the map every ML engineer should carry in 2026:

1️⃣ MEAN ABSOLUTE ERROR (MAE)
Average miss, easy to explain. On average, we're off by 5 units.

2️⃣ MEAN SQUARED ERROR (MSE)
Squares mistakes → big errors hurt more.

3️⃣ ROOT MEAN SQUARED ERROR (RMSE)
Square root of MSE. Same unit as the target, easier to relate.

4️⃣ R² COEFFICIENT
Explains how much variation your model captures. But don't confuse fit with usefulness.

5️⃣ ADJUSTED R²
Keeps R² honest. Extra useless features won't inflate the score.

6️⃣ MAPE (Mean Absolute Percentage Error)
Errors in percentages. Great for business dashboards, weak if actual values get near zero.

7️⃣ Huber Loss
Blends MAE & MSE. Punishes small errors like MSE, resists outliers like MAE.

8️⃣ Quantile Loss
Perfect when predicting ranges instead of single points like demand at the 90th percentile.

👁 VIEW

● = Actuals ○ = Predictions

MAE → avg |●-○|
MSE → avg (●-○)²
RMSE → √MSE
R² → variance explained
MAPE → % error
Huber → balance (MSE + MAE)
Quant → percentile accuracy

🏆 THE TAKEAWAY
Metrics decide what success looks like.
Choose wrong, and your good model is useless.
Choose right, and you build trust, adoption, and impact.

📝 TL;DR
MAE → simple error
MSE → punishes big errors
RMSE → interpretable scale
R² → fit, not prediction power
Adj R² → guards against overfitting
MAPE → % view, fragile near zero
Huber → outlier-resistant
Quantile → forecasts ranges

#MachineLearning #DataScience #RegressionMetrics #MLOps #AI #TechTips

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

🔖 5 Free Courses on AI Agents

1. https://huggingface.co/learn/agents-course — AI Agents Course 🤗

2. https://deeplearning.ai/courses/ai-agents-in-langgraph — AI Agents in LangGraph 🧠

3. https://deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/ — Multi AI Agent Systems with CrewAI 🤖

4. https://microsoft.github.io/AI-For-Beginners/agentic-ai/ — AI Agents for Beginners 🚀

5. https://deeplearning.ai/courses/building-code-agents-with-hugging-face-smolagents — Building Code Agents with Hugging Face smolagents 💻

If you want to learn about Agentic AI, save this collection. 💾

#AI #ArtificialIntelligence #MachineLearning #TechNews #FreeCourses #LearnAI

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Most people memorize CNN equations without truly understanding what the convolution operation is actually doing.

Here's what happens during a CNN forward pass in under 60 seconds:

🔹 Kernel (Filter) Setup:
A 3 × 3 kernel (filter) slides across the input matrix.

🔹 Element-Wise Multiplication:
At each position, the kernel multiplies its weights with the overlapping input values and sums the results to produce a single scalar output (z₁, z₂, z₃, z₄).

🔹 Stride:
With a stride of 2, the kernel moves two steps horizontally and vertically, creating a compressed 2 × 2 feature map.

🔹 Flattening & Prediction:
The feature map is flattened into a 1D vector, which is then passed through the remaining network layers to generate the final prediction (ŷ). This prediction is used to compute the loss (L).

📌 Save this post so you can quickly review how CNNs perform convolution before your next Deep Learning or Computer Vision interview.

✈️ Share this reel with an AI engineer, student, or anyone learning Deep Learning who wants to visualize how CNNs actually work.

C: far1din

Credits to the original creator.
Shared for inspiration and educational purposes only.
If you are the copyright owner and prefer this content to be removed, please send a DM and it will be removed respectfully.

#ConvolutionalNeuralNetworks #DeepLearning #ComputerVision #MachineLearning #AIEducation

"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

#MachineLearning #DeepLearning #AI #Mathematics #DataScience #NeuralNetworks

✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk

⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A