Master PyTorch Faster with These Free Resources!
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:

1. PyTorch Cheatsheet
A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
https://www.dropbox.com/scl/fi/e4xngykrfoubiw3xnd6fz/PyTorch-Cheatsheet.pdf?rlkey=vgx38ckps7aie120imgozgq4g&e=2&st=hgs06d4t&dl=0

2. Learn PyTorch Deep Learning with Hands-On Code
A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
Download:
https://www.dropbox.com/scl/fi/lfo7r6fnd8wjm3gp0jteh/Learn-PyTorch-Deep-Learning-with-Hands-On-Code.pdf?rlkey=mg9cxg41yerouzp0rklm8hqa2&e=2&st=c7k7rgay&dl=0

Save them, share them, and start building smarter models today!

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Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

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Open Guide to Data Structures and Algorithms

A must-read for anyone starting their journey in computer science and programming. This open-access book offers a clear, beginner-friendly introduction to the core concepts of data structures and algorithms, with simple explanations and practical examples. Whether you're a student or a self-learner, this guide is a solid foundation to build your DSA knowledge. Highly recommended for those who want to learn efficiently and effectively.

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A Complete Course to Learn Robotics and Perception

Notebook-based book "Introduction to Robotics and Perception" by Frank Dellaert and Seth Hutchinson

github.com/gtbook/robotics

roboticsbook.org/intro.html

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📕 A Course in Reinforcement Learning by Dimitri P. Bertsekas

Explore the comprehensive world of Reinforcement Learning (RL) with this authoritative textbook by Dimitri P. Bertsekas. This book offers an in-depth overview of RL methodologies, focusing on optimal and suboptimal control, as well as discrete optimization. It's an essential resource for students, researchers, and professionals in the field.

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Gradio: The easiest way to demo your models.

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- No frontend skills needed. It's all #Python.

- Works with any Python code, including custom functions.

- Share via temporary links or deploy on #HuggingFace Spaces.

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♻️ Repost if you found this useful

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This channels is for Programmers, Coders, Software Engineers.

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Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥

Description:
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فرصة عمل عن بعد 🧑‍💻
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Mastering CNNs: From Kernels to Model Evaluation

If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’s break it down from basic to advanced.

1. What is Conv2D?

Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.

2. What is a Kernel (or Filter)?

A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.

A 3x3 kernel means the filter looks at 3x3 chunks of the image.

The kernel detects patterns like edges, textures, etc.


Example:
A vertical edge detection kernel might look like:

[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]

3. What Are Filters in Conv2D?

In CNNs, we don’t use just one filter—we use multiple filters in a single Conv2D layer.

Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).

So if you have 32 filters in the Conv2D layer, you’ll get 32 feature maps.

More Filters = More Features = More Learning Power

4. Kernel Size and Its Impact

Smaller kernels (e.g., 3x3) are most common; they capture fine details.

Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.

Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.

5. Life Cycle of a CNN Model (From Data to Evaluation)

Let’s visualize how a CNN model works from start to finish:

Step 1: Data Collection

Images are gathered and labeled (e.g., cat vs dog).

Step 2: Preprocessing

Resize images

Normalize pixel values

Data augmentation (flipping, rotation, etc.)

Step 3: Model Building (Conv2D layers)

Add Conv2D + Activation (ReLU)

Use Pooling layers (MaxPooling2D)

Add Dropout to prevent overfitting

Flatten and connect to Dense layers

Step 4: Training the Model

Feed data in batches

Use loss function (like cross-entropy)

Optimize using backpropagation + optimizer (like Adam)

Adjust weights over several epochs

Step 5: Evaluation

Test the model on unseen data

Use metrics like Accuracy, Precision, Recall, F1-Score

Visualize using confusion matrix

Step 6: Deployment

Convert model to suitable format (e.g., ONNX, TensorFlow Lite)

Deploy on web, mobile, or edge devices

Summary

Conv2D uses filters (kernels) to extract image features.

More filters = better feature detection.

The CNN pipeline takes raw image data, learns features, and gives powerful predictions.

If this helped you, let me know! Or feel free to share your experience learning CNNs!

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🚀 Master the Transformer Architecture with PyTorch! 🧠

Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".

🔗 Check it out here:
https://www.k-a.in/pyt-transformer.html

This guide offers:

🌟 Detailed explanations of each component of the Transformer architecture.

🌟 Step-by-step code implementations in PyTorch.

🌟 Insights into the self-attention mechanism and positional encoding.

By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.

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Evolution of Deep Learning by Hand ✍️

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