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Related lectures (35)
Neural Networks for NLP
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Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Deep Learning Fundamentals
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Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.
Neural Networks: Regression and Classification
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Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
Deep Learning
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Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Neural Networks: Basics and Applications
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Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
Deep Learning: Data Representations and Neural Networks
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Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Reinforcement Learning Concepts
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Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Improving Models of the Ventral Visual Pathway
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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Deep Learning: Convolutional Networks
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Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.
Multi-layer Neural Networks
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Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.
Convolutional Neural Networks: Fundamentals
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Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.
Deep Neural Networks
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Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Deep Learning: Data Representations and Neural Networks
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Explores data representations, histograms, neural networks, and deep learning concepts.
Deep Learning: Graphs and Transformers Overview
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Covers deep learning concepts, focusing on graphs, transformers, and their applications in multimodal data processing.
Learning Sparse Features: Overfitting in Neural Networks
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Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.
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