Forwarded from Machine Learning
This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide."
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
https://t.me/MachineLearning9🤩
It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning.
https://github.com/Nicolepcx/transformers-the-definitive-guide
https://t.me/MachineLearning9
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Forwarded from Free Online Courses
🎓 Deep Learning for Text with PyTorch: NLP & Transformers
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📝 Course Details:
Natural Language Processing has undergone massive advancements, and this advanced PyTorch course takes learners through the evolution of text modeling. Moving from standard tokenization and RNNs to modern Transformer architectures and attention mechanisms, it delivers a comprehensive blueprint for deep NLP application design.
Who It's For
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…
📢 Channel: https://t.me/Courses27
#Data_Science
#DataCamp
🏫 Platform: DataCamp
🆓 100% FREE
━━━━━━━━━━━━━━━━━━━━
📝 Course Details:
Natural Language Processing has undergone massive advancements, and this advanced PyTorch course takes learners through the evolution of text modeling. Moving from standard tokenization and RNNs to modern Transformer architectures and attention mechanisms, it delivers a comprehensive blueprint for deep NLP application design.
Who It's For
Advanced machine learning engineers and NLP specialists who want to master PyTorch text preprocessing, recurrent networks, Transformers, and transfer learning.
Key Takeaways
• Text Preprocessing & Encodings: Master tokenization, stemming, lemmatization, One-Hot, Bag-of-Words, and TF-IDF encodings for neural networks.
…
📢 Channel: https://t.me/Courses27
Python for Data Science Cheat Sheet.pdf
372.3 KB
👨🏻💻 This file is a "comprehensive cheat sheet" for data scientists. Whenever you forget how to join data or customize a chart while coding, just refer to it.
⬅️ Chapter 1: All NumPy functions for creating arrays and broadcasting.
⬅️ Chapter 2: Everything about Pandas, from selecting rows and columns (loc/iloc) to handling time series.
⬅️ Chapter 3: A complete catalog of charts (scatter plots, bar charts, histograms, pie charts).
⬅️ Chapter 4: The golden section! A summary table listing all the important commands in one place.
https://t.me/CodeProgrammer
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