The Problem of Overfitting

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Neural Networks: Perceptron Model and Backpropagation Algorithm

Covers the perceptron model and backpropagation algorithm in neural networks.

Deep Learning

Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Neural Networks: Perceptron and Backpropagation

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

Polynomial Networks: Spectral Bias Analysis

Delves into the spectral bias of polynomial neural networks, analyzing the impact on learning different frequencies and discussing experimental results.

Optimization in Machine Learning

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Statistical Learning: Fundamentals

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Regularization by Early Stopping

Explores regularization by early stopping in deep neural networks to control flexibility and avoid overfitting.

Deep Learning Building Blocks

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