Machine Learning Accelerators: Types and Efficiency

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Recurrent Neural Networks: Training and Challenges

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

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Deep Neural Networks: Optimization and Approximation

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

Neural Networks: Perceptron and Backpropagation

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

Neural Networks: Regression and Classification

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

Building Physical Neural Networks

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

Neural Networks Recap: Activation Functions

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

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.

Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.