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Related lectures (31)

Neural Networks: Multi-layers

Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Neural Network Training

Covers the training process of a neural network, including feedforward, cost function, gradient checking, and visualization of hidden layers.

Adaptive Gradient Methods: Theory and Applications

Explores adaptive gradient methods, their properties, convergence, and comparison with traditional optimization algorithms.

Optimizing Loss Functions: Gradient Descent Variants

Explores early stopping in gradient descent and variants for faster optimization.

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.

Gradient Descent Log in to Mediaspace to watch this video

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Coordinate Descent: Optimization Strategies Log in to Mediaspace to watch this video

Explores coordinate descent optimization strategies, emphasizing simplicity in optimization through one-coordinate updates and discussing the implications of different approaches.

Deep Learning for Autonomous Vehicles Log in to Mediaspace to watch this video

Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.

Deep Learning Log in to Mediaspace to watch this video

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

Gradient Descent: Linear Regression Log in to Mediaspace to watch this video

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Neural Networks: Perceptron and Backpropagation Log in to Mediaspace to watch this video

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

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