Lectures related to Perceptrons (book)

Neural Networks: Multilayer Perceptrons

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

Neural Networks: Regression and Classification

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.

Pytorch Intro: MNIST and Digits

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

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Computer Vision History Recap

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

Committee Machine: Statistical Physics Approach

Explores hidden variables, graphical models, and computational gaps in neural network learning.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Introduction to Neural Networks

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

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Introduction to Learning by Stochastic Gradient Descent: Simple Perceptron

Covers the derivation of the stochastic gradient descent formula for a simple perceptron and explores the geometric interpretation of classification.

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: Logic and Applications

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

Neural Networks: Single-Layer Control

Explores the implementation of single-layer neural network controllers and the impact of sensor numbers on behavior complexity.

Sensory learning with neural networks

Explores sensory representations, perception, memory, cognitive control, and neural networks.

Introduction to Supervised Learning: Classification and Perceptrons

Explores supervised learning through classification as a geometric problem and the concept of finding a separating surface.

Deep Learning: Data Representations and Neural Networks

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

Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Deep Learning: Multilayer Perceptron and Training

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

Neural Networks: Multilayer Perceptron

Explores the history, models, training, convergence, and limitations of neural networks, including the backpropagation algorithm and universal approximation.