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Deep Learning Building Blocks
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Related lectures (48)
Convolutional Neural Networks: Filters and Channels
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Explores weight sharing, filters, channels, and data augmentation in convolutional neural networks.
Convolutional Networks: Overview and Architecture
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Covers the motivation and architecture of convolutional networks, from LeNet to AlexNet.
Introduction to Data Science
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Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Machine Learning and Modern AI: SWOT Analysis
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Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.
Deep Learning: Data, Models, and Challenges
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Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.
Deep Learning: Dimensionality and Data Representation
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Delves into deep learning's dimensionality, data representation, and performance in classifying large-dimensional data, exploring the curse of dimensionality and the neural tangent kernel.
Data Representations and Processing in Machine Learning
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Covers data representations and processing techniques essential for effective machine learning algorithms.
Machine Learned Interatomic Potentials
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Delves into machine learned interatomic potentials, showcasing their accuracy and cost-effectiveness in predicting chemical properties.
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