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Towards Data Science 🧬 Deep Learning 3d ago

PyTorch and TensorFlow tensor broadcasting: how silent shape errors cause difficult-to-debug machine learning bugs The post Silent Broadcasting Can Ruin Your Mo

Towards Data Science 🧬 Deep Learning 3w ago

The Sigmoid Function: From 'e' to Neural Networks

We use the equation all the time. But where did it actually come from? The post The Sigmoid Function: From 'e' to Neural Networks appeared first on Towards Data

Medium · AI 🧬 Deep Learning ⚡ AI Lesson 1mo ago

Why Andrej Karpathy’s makemore Series is the Ultimate Deep Learning Masterclass

How building bigrams, MLPs, WaveNets, and Transformers line-by-line turns black-box AI magic into pure intuition — Without doubt the best… Continue reading on T

Towards Data Science 🧬 Deep Learning 1mo ago

Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick

A clear, math-first walkthrough of how VAEs learn to generate new data The post Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparamet

Dev.to AI 🧬 Deep Learning 1mo ago

40% Adam Optimizer Improvement: PyTorch Benchmarks

40% Adam Optimizer Improvement If you are training deep learning models in PyTorch, default optimizer implementations leave performance on the table. Check out

Dev.to AI 🧬 Deep Learning 1mo ago

Conditional Flow Variational Autoencoders for Structured Sequence Prediction

Medium · Programming 🧬 Deep Learning ⚡ AI Lesson 2mo ago

Building a Custom C++/CUDA Deep Learning Framework to Beat PyTorch’s Speed

How I stripped away the abstractions of modern AI tools to build Aakaar from scratch. Continue reading on Medium »

Medium · AI 🧬 Deep Learning 2mo ago

Building a CNN Model for Chest X-ray Pneumonia Detection Using TensorFlow/Keras

SEO Keywords Continue reading on Medium »

Dev.to AI 🧬 Deep Learning 2mo ago

Seismic facies recognition based on prestack data using deep convolutionalautoencoder

Dev.to AI 🧬 Deep Learning 2mo ago

Advancing End-to-End Pixel Space Generative Modeling via Self-supervisedPre-training

Dev.to AI 🧬 Deep Learning 3mo ago

Overcoming the vanishing gradient problem in plain recurrent networks

Dev.to AI 🧬 Deep Learning 3mo ago

Molecular Generation with Recurrent Neural Networks (RNNs)

Dev.to AI 🧬 Deep Learning 3mo ago

A Comparison of LSTM and BERT for Small Corpus

Medium · AI 🧬 Deep Learning ⚡ AI Lesson 3mo ago

10 Must-Know Interview Questions on Deep Learning Models

CNNs, Transformers, RNNs, Autoencoders & More — Explained in Plain English Continue reading on Medium »

Dev.to AI 🧬 Deep Learning 4mo ago

Fully Convolutional Neural Networks for Crowd Segmentation

Dev.to AI 🧬 Deep Learning 4mo ago

The Power of Sparsity in Convolutional Neural Networks

Dev.to AI 🧬 Deep Learning 4mo ago

Block-Sparse Recurrent Neural Networks

Dev.to AI 🧬 Deep Learning 4mo ago

Scale-Invariant Convolutional Neural Networks

Dev.to AI 🧬 Deep Learning 4mo ago

RobustTAD: Robust Time Series Anomaly Detection via Decomposition andConvolutional Neural Networks

Medium · AI 🧬 Deep Learning ⚡ AI Lesson 4mo ago

The Deep Learning Compendium: From Mathematical Foundations to Modern Architectures

In the technology sector, Deep Learning (DL) is the engine driving the transition from static code to adaptive intelligence. Continue reading on Medium »

Medium · AI 🧬 Deep Learning 4mo ago

Activation Functions: Why Neural Networks Need Them

Hello everyone  Continue reading on Medium »

Dev.to AI 🧬 Deep Learning 4mo ago

Effectiveness of self-supervised pre-training for speech recognition

Dev.to AI 🧬 Deep Learning 4mo ago

Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition

Medium · Programming 🧬 Deep Learning ⚡ AI Lesson 5mo ago

Engineering Realism from Noise: A Deep Dive into DCGAN and WGAN-GP:

Introduction: The idea that a neural network can generate realistic images from pure randomness is both fascinating and counterintuitive… Continue reading on Me