Neural Networks: Two Layers Neural Network

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Neural Networks: Training and Activation

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

Deep Learning Fundamentals

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

Deep Learning: Convolutional Neural Networks

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

Deep Learning Fundamentals

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

Numerical analysis

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

Deep Neural Networks: Optimization and Approximation

Explores optimization and approximation in deep neural networks, including optimal control and numerical experiments.

Deep Learning: Convolutional Networks

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

Deep Learning

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

Feedforward Neural Networks: Activation Functions and Backpropagation

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

Deep Learning: Convolutional Neural Networks

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.