Deep Learning: Data, Models, and Challenges

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Related lectures (32)

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Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Introduction to Machine Learning

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

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Deep Learning: Principles and Applications

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

Introduction to Data Science

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

Deep Learning: Data Representations and Neural Networks

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Deep Learning Fundamentals

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

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

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

Deep Neural Networks

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.