Deep Learning

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Forward-Only Convolutional Neural Networks with Learnable Channel-Class Assignment

 🤖AI  Content type: Academic
arxiv.org·

New comment by Ishan1907 in "Ask HN: Who wants to be hired? (June 2026)"

 🤖Machine Learning

Efficient and accurate neural-field reconstruction using resistive memory

 🤖AI  Content type: Academic
nature.com
·

OpenCV 5 brings new deep neural network engine, stronger ONNX support, and faster core

 👁️Computer Vision
alternativeto.net·

Agentic AI in Telecommunications: The Next Evolution of Network Management

 🤖Machine Learning  Content type: Discussion

meleantonio forked meleantonio/DEQN_Galo_03_2026 from sischei/DEQN_Galo_03_2026

 🤖AI  Content type: Code
github.com·

Deep learning reveals enhanced ENSO predictability under historical anthropogenic forcing

 🤖Machine Learning
science.org·

Machine learning from scratch, what to build before using scikit-learn

 🧠Neural Networks  Content type: Tutorial
iwtlp.com··DEV

The neural networks for structured datasets

 🤖AI  Content type: Blog
breno.bearblog.dev·

Backpropagation Without the Magic: A First-Principles Derivation

 🤖AI  Content type: Blog
medium.com
·

Are Neural Networks Collision Resistant?

 🤖AI
link.aps.org·

Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods

 🤖AI  Content type: Academic
arxiv.org·

**PyTorch Stochastic Gradient Optimization Technique**

 🤖AI
sitepoint.com·

CytoGem-XAI:A Hypergraph Neural Network Framework for Genome-Scale Metabolic Modeling and Interpretable Analysis

 🤖Machine Learning  Content type: Academic
biorxiv.org·

A deep learning framework for emotion recognition in music using multimodal data fusion

 🤖AI  Content type: Academic
nature.com·

Evaluating and developing machine learning models: äN introduction (gpn24)

 🤖AI
cdn.media.ccc.de·

ml-from-scratch-book/code: Companion code for Machine Learning From Scratch — 10 core ML algorithms built from scratch with NumPy, compared with Scikit-learn and PyTorch.

 🤖AI  Content type: Code
github.com··Hacker News

Neural Networks Are Pattern Matchers, Not Physicists.

 🤖AI  Content type: Blog
medium.com·

Optimal Rates for Generalization of Gradient Descent Methods with Deep Neural Networks

 🤖Machine Learning  Content type: Academic
arxiv.org·

Theory of learning of high-dimensional controlled non-linear dynamical systems (I): models and methods

 🧠Neural Networks  Content type: Academic
arxiv.org·

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