Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Neural Networks: Single-Layer Control
Graph Chatbot
Related lectures (32)
Neural Networks: Multilayer Perceptrons
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Deep Neural Networks: Optimization and Approximation
Explores optimization and approximation in deep neural networks, including optimal control and numerical experiments.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Neural Networks: Perceptron
Covers the main concepts of neural networks, including the Perceptron model and training algorithms.
Pytorch Intro: MNIST and Digits
Covers Pytorch basics with MNIST and Digits datasets, focusing on training neural networks for handwritten digit recognition.
Deep Learning: Convolutional Neural Networks
Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.
Kernel Methods: Neural Networks
Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.
General Introduction to Artificial Neural Networks
Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.
Multilayer Networks: First Steps
Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.
Neural Networks
Explores neural networks, hidden layers, weight adjustments, activation functions, and the universal approximation theorem.
Introduction to Neural Networks
Introduces neural networks, focusing on multilayer perceptrons and training with stochastic gradient descent.
Neural Networks: Two-layer Networks and Backpropagation
Explores two-layer neural networks and backpropagation for learning feature spaces and approximating continuous functions.
Bio-Inspired Learning: Neural Networks, Genetic Algorithms
Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.
XOR Problem: Neural Networks
Delves into solving the XOR problem using a two-layer neural network.
Neural Networks: Basics and Applications
Log in to Mediaspace to watch this video
Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Neural Networks: Logic and Applications
Log in to Mediaspace to watch this video
Explores the logic of neuronal function, the Perceptron model, deep learning applications, and levels of abstraction in neural models.
Deep Learning: Data Representations and Neural Networks
Log in to Mediaspace to watch this video
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Deep Learning: Multilayer Perceptron and Training
Log in to Mediaspace to watch this video
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Neural Networks: Perceptron Model and Backpropagation Algorithm
Log in to Mediaspace to watch this video
Covers the perceptron model and backpropagation algorithm in neural networks.
Neural Taskonomy and Historical Perspectives in Visual Intelligence
Log in to Mediaspace to watch this video
Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.
Previous
Page 1 of 2
Next