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Crash course on Deep Learning

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Related lectures (37)

Deep Learning Fundamentals Log in to Mediaspace to watch this video

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

Deep Learning: Convolutional Networks Log in to Mediaspace to watch this video

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

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.

Neural Networks: Regression and Classification Log in to Mediaspace to watch this video

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

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

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

Deep Learning: Multilayer Perceptron and Training Log in to Mediaspace to watch this video

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

Deep Learning: Convolutional Neural Networks and Training Techniques Log in to Mediaspace to watch this video

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

Neural Networks: Basics and History Log in to Mediaspace to watch this video

Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.

Neural Networks: Basics and Applications Log in to Mediaspace to watch this video

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Multi-layered Perceptron: History and Training Algorithm Log in to Mediaspace to watch this video

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

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Explores data representations, histograms, neural networks, and deep learning concepts.

Neural Network Approximation and Learning Log in to Mediaspace to watch this video

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Learning from the Interconnected World with Graphs Log in to Mediaspace to watch this video

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Modeling the Brain: Machine Learning and Neuroscience Log in to Mediaspace to watch this video

Explores the synergy between machine learning and neuroscience, showcasing how deep neural networks can predict neural responses and the challenges faced by AI in robotics.

Building Neural Networks: Assembling Brain Regions Log in to Mediaspace to watch this video

Discusses assembling neural networks by defining space and populating it with neurons, emphasizing the challenges and strategies for accurate morphologies and volume information.

Optimization Techniques: Stochastic Gradient Descent and Beyond Log in to Mediaspace to watch this video

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Optimization Basics: Unconstrained Optimization and Gradient Descent Log in to Mediaspace to watch this video

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

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