Forwarded from Machine Learning with Python
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Hugging Face Viewer is now at 2300 viewable models! 😊 Would love more feedback and ideas!
It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀
Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!
🌟 hfviewer.com
#HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz
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It's a free interactive graph visualizer for learning about the architectures of open source AI models! 🚀
Hovering nodes in the graph links to a definitions + animation and the paper that introduced it!
🌟 hfviewer.com
#HuggingFace #AI #MachineLearning #OpenSource #TechNews #DataViz
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🔖 A large collection of lectures on Machine Learning and Deep Learning 🧠
We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖
Excellent material for both learning and reviewing key topics. 📚
⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle
#MachineLearning #DeepLearning #AI #Tech #Coding #Learning
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We found a repository that brings together high-quality materials on several areas of artificial intelligence. 🤖
Excellent material for both learning and reviewing key topics. 📚
⛓️ Link to GitHub
https://github.com/kmario23/deep-learning-drizzle
#MachineLearning #DeepLearning #AI #Tech #Coding #Learning
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Get a job or employment opportunity by using our smart bot that connects the right person to the right job.
After using the bot, click the Find Job button.
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After using the bot, click the Find Job button.
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Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers
🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity
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🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars.
📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context.
📖 It contains 20 chapters:
* Vectors, matrices, calculus
* Statistics and probability
* Machine learning and deep learning
* NLP, computer vision, audio/speech
* Multimodal learning and autonomous systems
* GNN, OS, algorithms
* Production engineering, GPU/SIMD
* AI inference, ML systems design, and applied AI
🤖 There is also a MCP server so that Claude Code, Cursor, VS Code, and other AI assistants can use the compendium as a local knowledge base.
💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI.
🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium
#AI #MachineLearning #ComputerScience #Maths #OpenSource #DevCommunity
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Follow the Ai Tools Daily channel on WhatsApp:
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sequence of four inputs, carrying every hidden state forward yourself. 🔄
1. Given
Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊
2. Initialize
Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑
3. First hidden layer (a)
We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮
4. Second hidden layer (b)
Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️
5. Third hidden layer (c)
Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁
6. Output layer (y)
Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝
7. Carry the states forward
We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀
8. Process X2
Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢
9. Carry the states forward
Let us copy a2, b2, c2 across, exactly as before. 🔄
10. Process X3
Same four moves, third input. Y3 = [13, -1, 9]. 🧩
11. Carry the states forward
We copy a3, b3, c3 across, one last time. ⏭️
12. Process X4
Repeat once more. Y4 = [15, 7, 2]. ✅
You have just run a Deep RNN over a whole sequence by hand. ✍️
The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]
The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠
#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
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1. Given
Four inputs X1 to X4, recurrent weights and biases for hidden layers a, b, c, and an output layer y. 📊
2. Initialize
Let us set the hidden states a0, b0, c0 to zeros. Nothing has been read yet. 🛑
3. First hidden layer (a)
We build the transformation matrix by laying the input weights, the state weights and the biases side by side. We stack X1, the previous state a0, and an extra 1 underneath. Multiply the two, and a1 = [0, 1]. 🧮
4. Second hidden layer (b)
Let us do it again, one layer up. Now a1 is the input, and b0 is the previous state. Multiply: b1 = [1, -1]. ⬆️
5. Third hidden layer (c)
Once more. b1 is the input, c0 is the previous state, and c1 = [1, 1]. 🔁
6. Output layer (y)
Let us read the answer off the top of the stack. Weights and biases against [c1; 1], and Y1 = [3, 0, 3]. 📝
7. Carry the states forward
We copy a1, b1, c1 across. This is the whole trick of a recurrent network: the states are the only thing the next input gets to see. 🚀
8. Process X2
Repeat steps 3 to 6 for the second input: three hidden layers, then the output. Y2 = [5, 0, 4]. 🔢
9. Carry the states forward
Let us copy a2, b2, c2 across, exactly as before. 🔄
10. Process X3
Same four moves, third input. Y3 = [13, -1, 9]. 🧩
11. Carry the states forward
We copy a3, b3, c3 across, one last time. ⏭️
12. Process X4
Repeat once more. Y4 = [15, 7, 2]. ✅
You have just run a Deep RNN over a whole sequence by hand. ✍️
The outputs:
Y1: [3, 0, 3]
Y2: [5, 0, 4]
Y3: [13, -1, 9]
Y4: [15, 7, 2]
The takeaway: the hidden states are the memory, and they are the only memory there is. Everything the network learns from X1 has to fit in those little two-cell columns and get handed forward, one step at a time. 🧠
#RNN #DeepLearning #AI #MachineLearning #NeuralNetworks #Tech
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16 GB RAM. No cloud subscription. Which local AI model actually fits?
How AI Helps built a free Telegram model picker. Choose your task, RAM or VRAM, language, runtime, and commercial-use requirement.
Then compare a shortlist by memory, license, sources, download options, and launch commands when available.
Join How AI Helps and open the pinned model-picker guide
How AI Helps built a free Telegram model picker. Choose your task, RAM or VRAM, language, runtime, and commercial-use requirement.
Then compare a shortlist by memory, license, sources, download options, and launch commands when available.
Join How AI Helps and open the pinned model-picker guide
❤5👍5
📚 Mathematics without fluff: Three free books for those who want a solid foundation
Three free mathematics books by Allen Hatcher 🧮
If you're looking for a solid foundation in topology, K-theory, and number theory, Allen Hatcher has an excellent free collection. 🎓
1. Algebraic Topology 📐
A classic textbook on algebraic topology. The book was published by Cambridge University Press, but the online version is available for free under an agreement with the publisher. The website offers the complete PDF, chapters individually, corrections, and additional exercises.
🔗 https://pi.math.cornell.edu/~hatcher/AT/AT.pdf
Additionally: Spectral Sequences - a separate, expanded chapter for this book.
🔗 https://pi.math.cornell.edu/~hatcher/AT/ATch5.pdf
2. Vector Bundles & K-Theory 🧶
A concise book about vector bundles, topological K-theory, and characteristic classes. Currently, approximately 120 pages are available online, covering the basics of vector bundles, a portion of K-theory, Bott periodicity, characteristic classes, and the stable J-homomorphism.
🔗 https://pi.math.cornell.edu/~hatcher/VBKT/VB.pdf
3. Topology of Numbers 🔢
An unusual introduction to number theory through geometry and pictures. It focuses heavily on quadratic forms, Farey diagrams, continued fractions, Pell's equation, quadratic reciprocity, and Conway's topograph. A PDF of approximately 350 pages is available for free.
🔗 https://pi.math.cornell.edu/~hatcher/TN/TNbook.pdf
#Mathematics #Topology #FreeBooks #Learning #STEM #Education
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Three free mathematics books by Allen Hatcher 🧮
If you're looking for a solid foundation in topology, K-theory, and number theory, Allen Hatcher has an excellent free collection. 🎓
1. Algebraic Topology 📐
A classic textbook on algebraic topology. The book was published by Cambridge University Press, but the online version is available for free under an agreement with the publisher. The website offers the complete PDF, chapters individually, corrections, and additional exercises.
🔗 https://pi.math.cornell.edu/~hatcher/AT/AT.pdf
Additionally: Spectral Sequences - a separate, expanded chapter for this book.
🔗 https://pi.math.cornell.edu/~hatcher/AT/ATch5.pdf
2. Vector Bundles & K-Theory 🧶
A concise book about vector bundles, topological K-theory, and characteristic classes. Currently, approximately 120 pages are available online, covering the basics of vector bundles, a portion of K-theory, Bott periodicity, characteristic classes, and the stable J-homomorphism.
🔗 https://pi.math.cornell.edu/~hatcher/VBKT/VB.pdf
3. Topology of Numbers 🔢
An unusual introduction to number theory through geometry and pictures. It focuses heavily on quadratic forms, Farey diagrams, continued fractions, Pell's equation, quadratic reciprocity, and Conway's topograph. A PDF of approximately 350 pages is available for free.
🔗 https://pi.math.cornell.edu/~hatcher/TN/TNbook.pdf
#Mathematics #Topology #FreeBooks #Learning #STEM #Education
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🚀 Stop Maintaining Scrapers. Start Shipping Products.
Build AI products, not scraping infrastructure.
CoreClaw provides ready-to-use Workers & APIs for 1000+ websites — including Google Maps, Instagram, Facebook, YouTube, Amazon, Tiktok and Google Search Scraper.
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Build AI products, not scraping infrastructure.
CoreClaw provides ready-to-use Workers & APIs for 1000+ websites — including Google Maps, Instagram, Facebook, YouTube, Amazon, Tiktok and Google Search Scraper.
✔️ No infrastructure
✔️ No proxy management
✔️ No scraper maintenance
✔️ JSON / CSV / REST API
🎁 Create a free account. Get free credits. Explore every Worker.
👉 https://coreclaw.com
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Here's a Python tool for accurately extracting text from PDFs and images into Markdown and JSON. 📄✨
It supports tables, formulas, multiple OCR engines (Marker, Surya-OCR, Tesseract) and has built-in personal data removal. 🔒🤖
https://github.com/CatchTheTornado/pdf-extract-api
#PDF #OCR #Python #Markdown #DataExtraction #TechTools
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It supports tables, formulas, multiple OCR engines (Marker, Surya-OCR, Tesseract) and has built-in personal data removal. 🔒🤖
https://github.com/CatchTheTornado/pdf-extract-api
#PDF #OCR #Python #Markdown #DataExtraction #TechTools
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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
🆓 Free Video Course
⏰ Duration: 30 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
🔗 Course Link
5-Day Gen AI Intensive Course with Google
🆓 Free Video + Hands-On Codelabs
⏰ Duration: 5-day structure
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Created by: Google & Kaggle
🔗 Course Link
Free GenAI 65-Hour Bootcamp
🆓 Free Video Course
⏰ Duration: 65 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructor: Andrew Brown (ExamPro) via freeCodeCamp
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: Multi-hour
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Great Learning Academy
🔗 Course Link
Introduction to Generative AI
🆓 Free Video Course
⏰ Duration: 45 min
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Google Skills
🔗 Course Link
Generative AI for Beginners
📚 Text Course
⏰ Duration: 21 lessons
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Microsoft Cloud Advocates
🔗 Course Link
AI Capabilities and Limitations
🆓 Free Video Course
⏰ Duration: Self-paced
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Anthropic Academy
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: 4 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Simplilearn
🔗 Course Link
Reading Materials
📖 Prompt Engineering Guide
📖 Awesome Generative AI (Curated Resource List)
📖 Generative AI: A Beginner's Guide
📖 Understanding Generative AI Capabilities
📖Stanford HAI: 2025 AI Index Report
Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More
🆓 Free Video Course
⏰ Duration: 30 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructors: Krish Naik, Sunny Savita & Boktiar Ahmed Bappy via freeCodeCamp
🔗 Course Link
5-Day Gen AI Intensive Course with Google
🆓 Free Video + Hands-On Codelabs
⏰ Duration: 5-day structure
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Created by: Google & Kaggle
🔗 Course Link
Free GenAI 65-Hour Bootcamp
🆓 Free Video Course
⏰ Duration: 65 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner to Intermediate
👨🏫 Instructor: Andrew Brown (ExamPro) via freeCodeCamp
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: Multi-hour
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Great Learning Academy
🔗 Course Link
Introduction to Generative AI
🆓 Free Video Course
⏰ Duration: 45 min
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Google Skills
🔗 Course Link
Generative AI for Beginners
📚 Text Course
⏰ Duration: 21 lessons
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Microsoft Cloud Advocates
🔗 Course Link
AI Capabilities and Limitations
🆓 Free Video Course
⏰ Duration: Self-paced
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Anthropic Academy
🔗 Course Link
Generative AI for Beginners
🆓 Free Video Course
⏰ Duration: 4 hrs
🏃♂️ Self Paced
📈 Difficulty: Beginner
👨🏫 Created by: Simplilearn
🔗 Course Link
Reading Materials
📖 Prompt Engineering Guide
📖 Awesome Generative AI (Curated Resource List)
📖 Generative AI: A Beginner's Guide
📖 Understanding Generative AI Capabilities
📖Stanford HAI: 2025 AI Index Report
YouTube
Generative AI Full Course – 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.…
Instructors: Krish Naik, Sunny Savita, and Boktiar Ahmed Bappy.…
❤3👍2🔥2
Python Courses & Resources
Free Generative AI Courses Generative AI Full Course: Gemini Pro, OpenAI, Llama, Langchain, Pinecone, Vector Databases & More 🆓 Free Video Course ⏰ Duration: 30 hrs 🏃♂️ Self Paced 📈 Difficulty: Beginner to Intermediate 👨🏫 Instructors: Krish Naik, Sunny…
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. 📚
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. 🐍
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. 💻
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. 🐍
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. 💻
https://foundations-of-applied-mathematics.github.io
#Mathematics #Python #Education #DataScience #Algorithms #Learning
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