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A Powerful Alternative to Pandas 🚀
This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️
To get started, simply replace a single import:
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️
To get started, simply replace a single import:
import fireducks.pandas as pd
Performance Benchmarks demonstrate speed improvements in various use cases. 📈
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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I kept running into the same problem: some of the best AI/ML books are legally free. The authors put them up on their own sites, but the links are scattered across personal pages, university sites, and random GitHub repos nobody finds.
So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.
Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄
It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝
Repo:
https://github.com/MarcosSete/awesome-free-ai-books
#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource
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So I built a single index: Awesome Free AI Books. 30+ books across Deep Learning, Reinforcement Learning, Bayesian/Probabilistic ML, NLP & LLMs, Math for ML, Computer Vision, Generative Models, Causal Inference, GNNs, and AI Safety. Think Goodfellow’s Deep Learning, Sutton & Barto’s RL bible, Murphy’s Probabilistic ML, Bishop’s latest, Jurafsky & Martin’s SLP3 draft, and more.
Every link points straight to the author’s or publisher’s own page—no rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. 🔄
It’s open source and open to contributions. If you know a legitimately free book that’s missing, PRs and issues are welcome. 🤝
Repo:
https://github.com/MarcosSete/awesome-free-ai-books
#AI #MachineLearning #DeepLearning #NLP #LLMs #OpenSource
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Day 7 of self-studying Berkeley CS189 — stochastic gradient descent notes 📚📝
🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
Where
📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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🔥 *Stochastic Gradient Descent (SGD)* is a powerful optimization algorithm used to minimize loss functions in machine learning. Unlike batch gradient descent, which uses the entire dataset to compute gradients, SGD updates parameters using a single training example (or a small mini-batch) at a time.
🚀 Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
📊 The Update Rule:
θ = θ - α * ∇J(θ; x⁽ⁱ⁾, y⁽ⁱ⁾)Where
α is the learning rate and (x⁽ⁱ⁾, y⁽ⁱ⁾) is a single training example.📌 Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
🧠 *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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