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Related lectures (52)

Machine Learning Basics Log in to Mediaspace to watch this video

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

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

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Feed-forward Networks Log in to Mediaspace to watch this video

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Learning Sparse Features: Overfitting in Neural Networks Log in to Mediaspace to watch this video

Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.

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.

Machine Learning Basics Log in to Mediaspace to watch this video

Covers machine learning basics, neural networks, CNNs, and model tuning.

Introduction to Data Science Log in to Mediaspace to watch this video

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

Deep Learning Fundamentals Log in to Mediaspace to watch this video

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Introduction to Machine Learning Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

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: Learning Features & Linear Prediction Log in to Mediaspace to watch this video

Explores neural networks' ability to learn features and make linear predictions, emphasizing the importance of data quantity for effective performance.

Deep Learning: Principles and Applications Log in to Mediaspace to watch this video

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Improving Models of the Ventral Visual Pathway Log in to Mediaspace to watch this video

Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.

Boltzmann Machine Log in to Mediaspace to watch this video

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.

Neural Taskonomy and Historical Perspectives in Visual Intelligence Log in to Mediaspace to watch this video

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

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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.

Optimal Errors and Phase Transitions Log in to Mediaspace to watch this video

Explores optimal errors and phase transitions in high dimensional models.

Multi-layer Neural Networks Log in to Mediaspace to watch this video

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

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