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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

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Shared for inspiration and educational purposes only.
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#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

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🔖 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