Neural Networks: Basics and Applications

Description

This lecture covers the fundamentals of neural networks, including artificial neurons, multilayer perceptrons, and regression. It explores solving the XOR problem, classification, and regularization techniques. Practical applications such as predicting weather data and fitting polynomials are demonstrated. The lecture emphasizes the importance of network flexibility, parameterization, and regularization in achieving accurate predictions.

Official source

https://mediaspace.epfl.ch/media/0_9hdj2x5w

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A Residual Neural Network (a.k.a. Residual Network, ResNet) is a deep learning model in which the weight layers learn residual functions with reference to the layer inputs. A Residual Network is a network with skip connections that perform identity mappings, merged with the layer outputs by addition. It behaves like a Highway Network whose gates are opened through strongly positive bias weights. This enables deep learning models with tens or hundreds of layers to train easily and approach better accuracy when going deeper.

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Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

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