🔥 Free IT Cert Resources – Grab Them While They're Hot!
🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity
💥No signup traps, no hidden fees – just click and download.
📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT
🪜 Online FREE Course → https://bit.ly/4vHFJSz
☁️ FREE AI Materials → https://bit.ly/4wdu7X6
📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity
💥No signup traps, no hidden fees – just click and download.
📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT
🪜 Online FREE Course → https://bit.ly/4vHFJSz
☁️ FREE AI Materials → https://bit.ly/4wdu7X6
📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
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🔥 Free IT Cert Resources – Grab Them While They're Hot!
🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity
💥No signup traps, no hidden fees – just click and download.
📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT
🪜 Online FREE Course → https://bit.ly/4vHFJSz
☁️ FREE AI Materials → https://bit.ly/4wdu7X6
📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
🌈SPOTO just dropped a bunch of 100% free study kits for 2026 – covering #Cisco, #AWS, #PMP, #AI, #Python, #Excel, and #Cybersecurity
💥No signup traps, no hidden fees – just click and download.
📘 FREE Cert E‑Book → https://bit.ly/4wkiLAT
🪜 Online FREE Course → https://bit.ly/4vHFJSz
☁️ FREE AI Materials → https://bit.ly/4wdu7X6
📊 Cloud Study Guide → https://bit.ly/4y0HyeW
🧠 Free Mock Exam → https://bit.ly/4ff8jos
Tag a friend who's also on this journey – Get certified together! 💪
🌐 Join the community: https://chat.whatsapp.com/FmbIbbqm2QhKglVpVTSH4d/
📲 Need personalized help? → https://wa.link/6k7042
❤1
🎓 Free Courses on Claude and AI From Anthropic
Anthropic has expanded its educational platform, Anthropic Academy. It now offers more than 20 free courses on using Claude and AI-powered tools.
No programming experience is required. There are programs for students, educators, nonprofits, and small businesses. The courses cover how to give models clear instructions, evaluate their responses, and automate everyday tasks.
Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.
All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.
📌 Participants receive official certificates after completing many of the programs.
➡️ Find all the courses here.
https://anthropic.com/learn/courses
Planning to take one?
#Anthropic #Claude #AICourses #FreeLearning #TechEducation #AI
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Anthropic has expanded its educational platform, Anthropic Academy. It now offers more than 20 free courses on using Claude and AI-powered tools.
No programming experience is required. There are programs for students, educators, nonprofits, and small businesses. The courses cover how to give models clear instructions, evaluate their responses, and automate everyday tasks.
Developers can also take dedicated courses on the Claude API, Claude Code, Model Context Protocol, Agent Skills, and cloud integrations.
All courses are free, and you don't need a Claude account to enroll. All materials and videos are in English.
📌 Participants receive official certificates after completing many of the programs.
➡️ Find all the courses here.
https://anthropic.com/learn/courses
Planning to take one?
#Anthropic #Claude #AICourses #FreeLearning #TechEducation #AI
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Reinforcement Learning Methods and Tutorials 🧠📚
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
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Forwarded from Machine Learning
Diving deep into Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP. 🤖🧠
Lectures: 🎓📚
https://github.com/kmario23/deep-learning-drizzle
#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP
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Lectures: 🎓📚
https://github.com/kmario23/deep-learning-drizzle
#DeepLearning #MachineLearning #AI #ReinforcementLearning #ComputerVision #NLP
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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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🔖 Comprehensive Practical Course on Reinforcement Learning
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
#ReinforcementLearning #TensorFlow #MachineLearning #DeepLearning #AI #Tech
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⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms.
The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study.
⛓️ Link to GitHub
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
#ReinforcementLearning #TensorFlow #MachineLearning #DeepLearning #AI #Tech
✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
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Forwarded from Machine Learning
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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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖
https://github.com/visenger/awesome-mlops
#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources
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https://github.com/visenger/awesome-mlops
#MLOps #MachineLearning #DevOps #AI #DataScience #TechResources
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U-Net by hand ✍️ ~ 17 steps walkthrough below
I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked!
It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL.
Now it is the backbone of diffusion models, which you see in almost all modern image generation models.
I drew the network as a C so the matrix multiplication flows naturally down.
Tilt your head to the right and it is a U again. 🤣
Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself.
= 1. Given =
An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use.
= 2. Convolution 1 =
Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map.
= 3. Find the maxima =
We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down.
= 4. Max pool 1 =
Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good.
= 5. Convolution 2 =
We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now.
= 6. Find the maxima again =
Same move as step 3, on the blue map.
= 7. Max pool 2 =
Eight columns become four.
= 8. The bottleneck =
Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept.
= 9. Spread it out =
We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them.
= 10. Transposed convolution 1 =
Let us fill those gaps by convolving over the spread-out grid. Four columns become eight.
= 11. The first skip =
We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away.
= 12. Convolution with the skip =
Let us convolve the upsampled features together with the copied ones.
= 13. Spread it out again =
Same as step 9, one level up.
= 14. Transposed convolution 2 =
Eight columns become sixteen, back to the width we started at.
= 15. The second skip =
The encoder's first feature map comes across, the one made before any pooling happened.
= 16. Convolution and ReLU =
We convolve, then cross out every negative and set it to zero.
= 17. Output convolution =
Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with.
The outputs:
Congrats! You just calculated a U-Net by hand.
💾 Save this post!
#UNet #DeepLearning #AI #NeuralNetworks #ComputerVision #MachineLearning
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I consider U-Net as a key milestone in deep learning, the first image-to-image model that really worked!
It came out of medical imaging, an unusual place, not from NeurIPS or CVPR or ACL.
Now it is the backbone of diffusion models, which you see in almost all modern image generation models.
I drew the network as a C so the matrix multiplication flows naturally down.
Tilt your head to the right and it is a U again. 🤣
Goal: push a 3 x 16 image down to a 2 x 4 bottleneck and back out again, filling in every cell yourself.
= 1. Given =
An image of three channels, R, G and B, sixteen pixels wide, and every kernel the network will use.
= 2. Convolution 1 =
Let us slide the first kernel over the image. Each output is one multiply-and-add over a 2 x 3 window, and the result is the green feature map.
= 3. Find the maxima =
We circle the largest value in each 1 x 2 window. Circling first is worth the extra step: it is the pooling decision, made before anything is written down.
= 4. Max pool 1 =
Let us copy those maxima down. Sixteen columns become eight, and half the detail is gone for good.
= 5. Convolution 2 =
We convolve again with the second kernel, deeper into the contracting path. The feature map is blue now.
= 6. Find the maxima again =
Same move as step 3, on the blue map.
= 7. Max pool 2 =
Eight columns become four.
= 8. The bottleneck =
Let us convolve once more. This is the bottom of the U, a 2 x 4 block that is everything the network kept.
= 9. Spread it out =
We start back up. The transposed convolution writes each bottleneck value into a wider grid, leaving gaps between them.
= 10. Transposed convolution 1 =
Let us fill those gaps by convolving over the spread-out grid. Four columns become eight.
= 11. The first skip =
We copy the encoder's matching row straight across. This is the skip connection, and it is the whole reason a U-Net can recover detail that pooling threw away.
= 12. Convolution with the skip =
Let us convolve the upsampled features together with the copied ones.
= 13. Spread it out again =
Same as step 9, one level up.
= 14. Transposed convolution 2 =
Eight columns become sixteen, back to the width we started at.
= 15. The second skip =
The encoder's first feature map comes across, the one made before any pooling happened.
= 16. Convolution and ReLU =
We convolve, then cross out every negative and set it to zero.
= 17. Output convolution =
Let us apply the last kernel. Out comes R', G' and B', an image the same size as the one we started with.
The outputs:
R' = [3, 0, 7, 0, 7, 0, 17, 0, 3, 0, 9, 0, 2, 0, 6, 0]
G' = [1, 20, 1, 10, 1, 12, 1, 19, 2, 5, 1, 11, 1, 3, 1, 7]
B' = [4, 20, 8, 10, 8, 12, 18, 19, 5, 5, 10, 11, 3, 3, 7, 7]
Congrats! You just calculated a U-Net by hand.
💾 Save this post!
#UNet #DeepLearning #AI #NeuralNetworks #ComputerVision #MachineLearning
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