Convolutional Layer: The Gradient

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

Image Processing II: Neural Networks and CNNs

Explores the evolution of CNNs in image processing, covering classical and deep neural networks, training algorithms, backpropagation, non-linear steps, loss functions, and software frameworks.

Tricks of the Trade in Deep Learning: Aims

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

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.

Neural Networks: Deep Neural Networks

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.