#Google #Colab notebooks to learn #Transformers 😎

Link: https://github.com/nlp-with-transformers/notebooks

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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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🚀 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.

#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks



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🧠💻📊

🔍 Understanding Recurrent Neural Networks (RNNs) Cheat Sheet!
Recurrent Neural Networks are a powerful type of neural network designed to handle sequential data. They are widely used in applications like natural language processing, speech recognition, and time-series prediction. Here's a quick cheat sheet to get you started:

📘 Key Concepts:
Sequential Data: RNNs are designed to process sequences of data, making them ideal for tasks where order matters.
Hidden State: Maintains information from previous inputs, enabling memory across time steps.
Backpropagation Through Time (BPTT): The method used to train RNNs by unrolling the network through time.

🔧 Common Variants:
Long Short-Term Memory (LSTM): Addresses vanishing gradient problems with gates to manage information flow.
Gated Recurrent Unit (GRU): Similar to LSTMs but with a simpler architecture.

🚀 Applications:
Language Modeling: Predicting the next word in a sentence.
Sentiment Analysis: Understanding sentiments in text.
Time-Series Forecasting: Predicting future data points in a series.

🔗 Resources:
Dive deeper with tutorials on platforms like Coursera, edX, or YouTube.
Explore open-source libraries like TensorFlow or PyTorch for implementation.
Let's harness the power of RNNs to innovate and solve complex problems! 💡

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What is torch.nn really?

When I started working with PyTorch, my biggest question was: "What is torch.nn?".

This article explains it quite well.

📌 Read

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Top 140 PyTorch Interview Questions and Answers

This comprehensive guide covers essential PyTorch interview questions across multiple categories, with detailed explanations for each.these 140 carefully curated questions represent the most important concepts you'll encounter in #PyTorch interviews.

🧠 Link: https://hackmd.io/@husseinsheikho/pytorch-interview

https://t.iss.one/CodeProgrammer

PyTorch 2.9 has been released, an update focused on performance, portability, and developer convenience.

The fresh version brings a stable libtorch ABI for C++/CUDA extensions, symmetric memory for multi-GPU kernels, extended wheel package support for ROCm, XPU, and #CUDA 13, as well as improvements for Intel, Arm, and x86 platforms.

The release includes 3216 commits from 452 contributors, and #PyTorch 2.9 continues to develop the open source #AI ecosystem worldwide.

Full analysis: https://hubs.la/Q03NNKqW0

👉 @codeprogrammer