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CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

#General, Artificial Intelligence, arXiv, D. Learning, Deep Learning (Article), Educación | 🇬🇧 Education, English, English (Articles), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Laboratory, Medium, news, Not numbered, Open Access

CNN Explainer tightly integrates a model overview that summarizes a CNN’s structure, and on-demand, dynamic visual explanation views that help users understand the underlying components of CNNs. Through smooth transitions across levels of abstraction, our tool enables users to inspect the interplay between low-level mathematical operations and high-level model structures.

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Auto Quantum Circuits

#Programmer, Artificial Intelligence, Educación | 🇬🇧 Education, English, English (Articles), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Laboratory, ML, news, Not numbered, Open Access, PCs Cuánticos | 🇬🇧 Quantum PCs, Programación y frameworks en IA | 🇬🇧 Programming and frameworks in AI, Quantum computing (Article), Research, Uncategorized

«AutoQML, self-assembling circuits, hyper-parameterized Quantum ML platform, using cirq, tensorflow and tfq. Trillions of possible qubit registries, gate combinations and moment sequences, ready to be adapted into your ML flow. Here I demonstrate climatechange, jameswebbspacetelescope and microbiology vision applications… [Thus far, a circuit with 16-Qubits and a gate sequence of [ YY ] – [ XX ] – [CNOT]

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Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

#Programmer, Advanced, Artificial Intelligence, Artificial Reality (Paper), arXiv, by, Creative Commons, Electrónica | 🇬🇧 Electronics, English (Articles), English (Paper), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, news, Not numbered, Open Access, Programación y frameworks en IA | 🇬🇧 Programming and frameworks in AI, Robótica | 🇬🇧 Robotics, Robotics (Article)

Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing

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Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch

#Datascience, articles, Artificial Intelligence, Ciencia de Datos | 🇬🇧 Data Science, Data Mining (Article), Data Science, Ebooks, Educación | 🇬🇧 Education, English, English (Articles), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Medium, news, Not numbered, Open Access, Programación y frameworks en IA | 🇬🇧 Programming and frameworks in AI

I was emboldened to write this book after my video series called Data Science With Julia got some traction. That too after a tweet about Decision Tree was liked by Julia Language itself. So I thought why not give it more?

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Human Learn

#Programmer, Advanced, Artificial Intelligence, D. Learning, Educación | 🇬🇧 Education, English (Articles), Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, Laboratory, Machine Learning (Article), MIT, news, Not numbered, Open Access, Uncategorized

Machine learning covers a lot of ground but it is also capable of making bad decision. We’ve also reached a stage of hype that folks forget that many classification problems can be handled by natural intelligence too. This package contains scikit-learn compatible tools that should make it easier to construct and benchmark rule based systems

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labml.ai Deep Learning Paper Implementations

#Programmer, Artificial Intelligence, D. Learning, Deep Learning (Article), Deep Learning (Tutorial), Educación | 🇬🇧 Education, English (Articles), English (Tutorial), Formación | 🇬🇧 Training, Inside The wall, InsidetheRss, Investigación | 🇬🇧 Research, MIT, news, Normal, Not numbered, Open Access, Research, Tutorials

This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.

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