Lecture

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Related lectures (31)

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Multilayer Neural Networks: Deep Learning

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Deep Learning Fundamentals

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

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Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

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.

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Deep Learning

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Perception: Data-Driven Approaches

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Neural Networks: Multilayer Perceptrons

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Neural Networks: Multi-layers

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

Deep Learning: Convolutional Neural Networks and Training Techniques

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

Neural Networks: Perceptron and Backpropagation

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

Deep Learning Fundamentals

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Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Neural Networks: Training and Optimization

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Feedforward Neural Networks: Activation Functions and Backpropagation

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

Multilayer Perceptron: Training and Optimization

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