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

🚨 STOP SCROLLING — YOUR DREAM JOB JUST GOT EASIER. 🚨

Meet Jobs204 — your 24/7 remote job hunter. 🤖

Every single day, I scan LinkedIn for fresh remote opportunities in:
🐍 Python • 🤖 AI/ML • 📊 Data Science • 🌐 Web Dev • 💻 Software Engineering

What you get:
⚡️ Daily drops — new listings posted in small batches
🎯 One-tap apply — tap "View & Apply" and you're on LinkedIn
📱 All-in-one hub — browse every role, anytime

Why it slaps:
Job hunting is exhausting. Refreshing LinkedIn 50 times a day? Nah. Jobs204 does the hunting for you — so you can focus on landing the offer. 💼

🎯 Try it now: https://t.iss.one/JobsFiBot?start=ref_channel

🔖 Google DeepMind has released a book titled "How to Scale Your Model."

It explains how to scale and deploy models even with limited computing resources.

It's useful for those who work on optimizing and deploying ML models.

⛓ Link to the book
https://jax-ml.github.io/scaling-book

🔖 A comprehensive collection of resources on MLOps

We've found a useful collection for those who want to go beyond just training models and delve into their deployment and operation.

This repository contains the best materials on deploying, monitoring, automating ML pipelines, CI/CD, and other practices that are essential for production systems.

⛓ Link to GitHub
https://github.com/visenger/awesome-mlops

🚨 SURPRISE ALERT! 🚨

Stop paying full price on Udemy. Seriously. 💸

I built a bot that hunts down 100% FREE Udemy coupons 24/7 — while you sleep, eat, or scroll. 🎯

Here's the magic:

📚 Mini App catalog — every active free coupon in one place
🔔 Auto-push — new courses land straight in your chat
📢 Live channel — never miss a deal

Why it matters?
Most people pay $200+ for courses you can grab for $0 — if you know where to look. Now you have a bot that does the looking for you. ⚡

🎓 Try it now: https://t.iss.one/UdemySybot?start=ref_channel

Your future self (and your wallet) will thank you. 💜

🔥 Land Your Dream Job – Free Interview Prep Resources Inside!

🌈Struggling with tough interview questions? Nervous about technical grilling? You're not alone.

We've just released a bunch of 100% free interview prep kits for 2026 – covering common Q&As, behavioral questions, technical deep-dives, and role-specific tips for #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity.

💥No signup traps, no hidden fees – just click and download.

🎯 Interview Question Bank → https://bit.ly/4xzKG0o
📘 Free Cert E‑Book → https://bit.ly/4zffDZp
🪜 Free Online Course → https://bit.ly/3TPLkbl
☁️ Free AI Materials → https://bit.ly/4q7T7gR
📊 Cloud Study Guide → https://bit.ly/4wbsjgV

Tag a friend who's also job-hunting – Ace together! 💪

🌐 Join the community: https://chat.whatsapp.com/FQOG04r9xSiIa2ElhaNUJU
🌐Join SPOTO telegram Group: https://t.iss.one/spotoITstudygroup
📲 Need personalized help? → https://wa.link/1zrbdh

🧿ANTHROPH\C Launches 13 Free AI Courses

Anthropic Academy has announced 13 free AI courses. The courses cover the following topics:

• Working with Claude
• AI Fundamentals
• AI Agents
• Model Context Protocol (MCP)
• Claude Code
• Working with APIs
• Enterprise AI
• Google Cloud Vertex AI and Amazon Bedrock Integration


1. Claude 101

2. AI Fluency: Framework & Foundations

3. Introduction to Agent Skills

4. Building with the Claude API

5. Claude Code in Action

6. Introduction to Model Context Protocol (MCP)

7. MCP: Advanced Topics

8. AI Fluency for Students

9. AI Fluency for Educators

10. Teaching AI Fluency

11. AI Fluency for Small Businesses

12. Claude with Amazon Bedrock

13. Claude with Google Cloud Vertex AI

The courses are designed for a wide audience, from beginners to developers, educators, students, and business owners.

"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊

It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮

In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️

If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓

Save both of these resources. They are excellent materials if you're interested in understanding the mathematics and technical mechanisms of Bitcoin. 💾

The Mathematics of Bitcoin:
https://arxiv.org/pdf/2003.00001

Bitcoin and Cryptocurrency Technologies:
https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf

#Bitcoin #Mathematics #Cryptocurrency #Blockchain #Princeton #Tech

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

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