Lectures related to Universal Approximation Theorem: MLP

Neural Networks: Multilayer Learning

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

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.

Double Descent Curves: Overparametrization

Explores double descent curves and overparametrization in machine learning models, highlighting the risks and benefits.

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.

Supervised Learning: Classification and Regression

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Feedforward Neural Networks: Activation Functions and Backpropagation

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Numerical analysis

Covers advanced numerical analysis topics including deep neural networks and optimization methods.

Interpolation: Spatial Phenomena and Methods

Covers global and local deterministic interpolation methods in geographic information systems, discussing expert knowledge, method selection, and uncertainty estimation.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

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

Neural Networks: Regression and Classification Log in to Mediaspace to watch this video

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

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: 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.

Deep Learning: Convolutional Networks Log in to Mediaspace to watch this video

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Gradient Descent: Linear Regression Log in to Mediaspace to watch this video

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.