Deep Learning with Yacine on MSN

Backpropagation from scratch in Python – step by step neural network tutorial

Learn how backpropagation works by building it from scratch in Python! This tutorial explains the math, logic, and coding behind training a neural network, helping you truly understand how deep ...
SlashGear

A New Algorithm Beats Dijkstra's Time For The Shortest Path To Any Point In A Network

When Edsger W. Dijkstra published his algorithm in 1959, computer networks were barely a thing. The algorithm in question found the shortest path between any two nodes on a graph, with a variant ...
techxplore

VFF-Net algorithm provides promising alternative to backpropagation for AI training

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...
Microsoft

PZO: Pseudo-Zeroth-Order Algorithm for Training Deep Neural Networks

Zeroth-order Optimization (ZO) has received wide attention in machine learning, especially when computing full gradient is expensive or even impossible. Recently, ZO has emerged as an important ...
Frontiers

Intelligent maneuver decision-making for UAVs using the TD3–LSTM reinforcement learning algorithm under uncertain information

Aiming to address the complexity and uncertainty of unmanned aerial vehicle (UAV) aerial confrontation, a twin delayed deep deterministic policy gradient (TD3)–long short-term memory (LSTM) ...
Frontiers

Pre-training, personalization, and self-calibration: all a neural network-based myoelectric decoder needs

Myoelectric control systems translate electromyographic signals (EMG) from muscles into movement intentions, allowing control over various interfaces, such as prosthetics, wearable devices, and ...
IEEE

SPOT: An Active Learning Algorithm for Efficient Deep Neural Network Training

Abstract: Recent advancements in deep neural networks heavily rely on large-scale labeled datasets. However, acquiring annotations for large datasets can be challenging due to annotation constraints.
Ars Technica

How a big shift in training LLMs led to a capability explosion

In April 2023, a few weeks after the launch of GPT-4, the Internet went wild for two new software projects with the audacious names BabyAGI and AutoGPT. “Over the past week, developers around the ...
IEEE

Optimizing Artificial Neural Networks Using Mountain Gazelle Optimizer

Abstract: The performance of artificial neural networks heavily depends on the optimization of network parameters, specifically weights and biases, during the ...