Lecture

Committee Machine: Statistical Physics Approach

Related lectures (31)

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Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Pytorch Intro: MNIST and Digits

Covers Pytorch basics with MNIST and Digits datasets, focusing on training neural networks for handwritten digit recognition.

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Neural Networks: Training and Activation

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

Deep Learning: Multilayer Perceptron and Training

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Computer Vision History Recap

Offers a historical overview of computer vision, exploring key developments and influential figures in the field.

Statistical Physics of Learning

Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Neural Networks: Logic and Applications

Explores the logic of neuronal function, the Perceptron model, deep learning applications, and levels of abstraction in neural models.

Introduction to Neural Networks

Introduces neural networks, focusing on multilayer perceptrons and training with stochastic gradient descent.

Neural Networks: Perceptron and Backpropagation

Covers the basics of neural networks, including the perceptron model and backpropagation.

Neural Networks: Perceptron

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Deep Learning Fundamentals

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

Neural Networks: Basics and History

Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Kernel Methods: Neural Networks

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