Neural Networks: Basics and History

This lecture introduces the basics of neural networks, focusing on their history and key concepts. Starting with the historical development of neural networks, the instructor covers the fundamental principles, including the mathematical model of a neuron and the artificial neuron. The lecture then delves into gradient descent, explaining how it is used to update the parameters of neural networks. Special attention is given to the multilayer perceptron (MLP) and its training phases. The presentation concludes with a discussion on the importance of cross-validation and the upcoming topic of building convolutional neural networks.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (89)

Related concepts (36)

ENV-540: Image processing for Earth observation

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Rectifier (neural networks)

In the context of artificial neural networks, the rectifier or ReLU (rectified linear unit) activation function is an activation function defined as the positive part of its argument: where x is the input to a neuron. This is also known as a ramp function and is analogous to half-wave rectification in electrical engineering. This activation function was introduced by Kunihiko Fukushima in 1969 in the context of visual feature extraction in hierarchical neural networks.

Kernel Methods: Neural Networks PHYS-467: Machine learning for physicists

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Neural Networks: Training and Activation CIVIL-226: Introduction to machine learning for engineers

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Neural Networks: Multilayer Perceptrons BIO-322: Introduction to machine learning for bioengineers

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Deep Learning Fundamentals ME-390: Foundations of artificial intelligence

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Neural Networks: Deep Neural Networks MATH-212: Analyse numérique et optimisation

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.