🔖 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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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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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
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📚 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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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.
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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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