Sensory learning with neural networks

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Related lectures (31)

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Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Biological and non-biological bodies

Explores the components of neuro robots and compares them to actual animals.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Theory of reinforcement learning: Introductory question

Covers the theory of reinforcement learning, exploring exploration/exploitation dilemma and continuous state/action spaces.

Neural Taskonomy and Historical Perspectives in Visual Intelligence

Covers Neural Taskonomy, the evolution of neural networks, and historical perspectives in visual intelligence.

Introduction to synaptic plasticity

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Neuroscience and Machine Learning: Understanding Visual Intelligence

Explores the relationship between neuroscience and machine learning in visual intelligence.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Neural Networks: Regression and Classification

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