Neural Networks: Basics and History

Related lectures (32)

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Covers the perceptron model and backpropagation algorithm in neural networks.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

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

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

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

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

Covers the main concepts of neural networks, including the Perceptron model and training algorithms.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

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