Here's a Python tool for accurately extracting text from PDFs and images into Markdown and JSON. πβ¨
It supports tables, formulas, multiple OCR engines (Marker, Surya-OCR, Tesseract) and has built-in personal data removal. ππ€
https://github.com/CatchTheTornado/pdf-extract-api
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It supports tables, formulas, multiple OCR engines (Marker, Surya-OCR, Tesseract) and has built-in personal data removal. ππ€
https://github.com/CatchTheTornado/pdf-extract-api
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Forwarded from Python Courses & Resources
Free Generative AI Courses
Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
π Free Video Course
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Free GenAI 65-Hour Bootcamp
π Free Video Course
β° Duration: 65 hrs
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π Difficulty: Beginner to Intermediate
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π Course Link
Generative AI for Beginners
π Free Video Course
β° Duration: Multi-hour
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Great Learning Academy
π Course Link
Introduction to Generative AI
π Free Video Course
β° Duration: 45 min
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Google Skills
π Course Link
Generative AI for Beginners
π Text Course
β° Duration: 21 lessons
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π Difficulty: Beginner
π¨βπ« Created by: Microsoft Cloud Advocates
π Course Link
AI Capabilities and Limitations
π Free Video Course
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π Difficulty: Beginner
π¨βπ« Created by: Anthropic Academy
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Generative AI for Beginners
π Free Video Course
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π Difficulty: Beginner
π¨βπ« Created by: Simplilearn
π Course Link
Reading Materials
π Prompt Engineering Guide
π Awesome Generative AI (Curated Resource List)
π Generative AI: A Beginner's Guide
π Understanding Generative AI Capabilities
πStanford HAI: 2025 AI Index Report
Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
π Free Video Course
β° Duration: 30 hrs
πββοΈ Self Paced
π Difficulty: Beginner to Intermediate
π¨βπ« Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
π Course Link
5-Day Gen AI Intensive Course with Google
π Free Video + Hands-On Codelabs
β° Duration: 5-day structure
πββοΈ Self Paced
π Difficulty: Beginner to Intermediate
π¨βπ« Created by: Google & Kaggle
π Course Link
Free GenAI 65-Hour Bootcamp
π Free Video Course
β° Duration: 65 hrs
πββοΈ Self Paced
π Difficulty: Beginner to Intermediate
π¨βπ« Instructor: Andrew Brown (ExamPro) via freeCodeCamp
π Course Link
Generative AI for Beginners
π Free Video Course
β° Duration: Multi-hour
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Great Learning Academy
π Course Link
Introduction to Generative AI
π Free Video Course
β° Duration: 45 min
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Google Skills
π Course Link
Generative AI for Beginners
π Text Course
β° Duration: 21 lessons
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Microsoft Cloud Advocates
π Course Link
AI Capabilities and Limitations
π Free Video Course
β° Duration: Self-paced
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Anthropic Academy
π Course Link
Generative AI for Beginners
π Free Video Course
β° Duration: 4 hrs
πββοΈ Self Paced
π Difficulty: Beginner
π¨βπ« Created by: Simplilearn
π Course Link
Reading Materials
π Prompt Engineering Guide
π Awesome Generative AI (Curated Resource List)
π Generative AI: A Beginner's Guide
π Understanding Generative AI Capabilities
πStanford HAI: 2025 AI Index Report
YouTube
Generative AI Full Course β Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
Learn about generative models and different frameworks, investigating the production of text and visual material produced by artificial intelligence. This course was originally recorded live.
Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.β¦
Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.β¦
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Python Courses & Resources
Free Generative AI Courses Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More π Free Video Course β° Duration: 30 hrs πββοΈ Self Paced π Difficulty: Beginner to Intermediate π¨βπ« Instructors: Krish Naik, Sunnyβ¦
Engaging with our posts can generate interest for others; even a small like could be the reason for someone else's success.
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Foundations of Applied Mathematics is a free series of four textbooks created for the applied and computational mathematics program at Brigham Young University. π
The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. π
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. π»
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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The series includes four volumes:
* Mathematical Analysis
* Algorithms, Approximation, and Optimization
* Uncertainty and Data
* Dynamics and Control
The series is suitable for upper-level undergraduate and introductory graduate students. It also includes Python lab exercises and practical assignments, connecting mathematical theory with numerical computation, algorithms, data analysis, and scientific applications. π
I particularly appreciate that these are not just theoretical textbooks. The accompanying Python materials help to illustrate how these concepts are applied to real-world computational problems. π»
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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π Stop Maintaining Scrapers. Start Shipping Products.
Build AI products, not scraping infrastructure.
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Build AI products, not scraping infrastructure.
CoreClaw provides ready-to-use Workers & APIs for 1000+ websites β including Google Maps, Instagram, Facebook, YouTube, Amazon, Tiktok and Google Search Scraper.
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Amplify (UK) plugs an AI agent straight into your Telegram, WhatsApp, Slack, WeChat, or Discord. Not just a GPT chat β an assistant that reaches into the real world.
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Amplify (UK) plugs an AI agent straight into your Telegram, WhatsApp, Slack, WeChat, or Discord. Not just a GPT chat β an assistant that reaches into the real world.
Handles it all: emails, reminders, spreadsheets, Telegram-channel digests, image and video generation, PDFs, Google Drive, Notion. Send it voice notes on the go β it gets everything.
Pricing: $10/mo + pay-as-you-go for the AI model, all costs transparent and tracked. Already have OpenAI subscription? Link it and skip paying for the model.
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A Powerful Alternative to Pandas π
This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. βοΈ
To get started, simply replace a single import:
Performance Benchmarks demonstrate speed improvements in various use cases. π
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. βοΈ
To get started, simply replace a single import:
import fireducks.pandas as pd
Performance Benchmarks demonstrate speed improvements in various use cases. π
More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing
#Pandas #Python #DataScience #Performance #Fireducks #BigData
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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 Goodfellowβs Deep Learning, Sutton & Bartoβs RL bible, Murphyβs Probabilistic ML, Bishopβs latest, Jurafsky & Martinβs SLP3 draft, and more.
Every link points straight to the authorβs or publisherβs own pageβno rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. π
Itβs open source and open to contributions. If you know a legitimately free book thatβs missing, PRs and issues are welcome. π€
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 Goodfellowβs Deep Learning, Sutton & Bartoβs RL bible, Murphyβs Probabilistic ML, Bishopβs latest, Jurafsky & Martinβs SLP3 draft, and more.
Every link points straight to the authorβs or publisherβs own pageβno rehosted PDFs, no shady mirrors. A weekly GitHub Action checks all links so they don't rot over time. π
Itβs open source and open to contributions. If you know a legitimately free book thatβs missing, PRs and issues are welcome. π€
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 β stochastic gradient descent notes ππ
π₯ *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.
π Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
π The Update Rule:
Where
π Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
π§ *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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π₯ *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.
π Key Benefits:
- Faster convergence on large datasets
- Escapes local minima more easily
- Suitable for online learning scenarios
π The Update Rule:
ΞΈ = ΞΈ - Ξ± * βJ(ΞΈ; xβ½β±βΎ, yβ½β±βΎ)Where
Ξ± is the learning rate and (xβ½β±βΎ, yβ½β±βΎ) is a single training example.π Challenges:
- High variance in updates
- Requires careful tuning of the learning rate
π§ *Tip:* Use momentum or adaptive learning rates (like Adam) to stabilize training!
#MachineLearning #CS189 #SGD #DeepLearning #DataScience #Algorithms
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