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Lecture
Deep Neural Networks and Splines
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Related lectures (32)
Deep Learning: Multilayer Perceptron and Training
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Multilayer Perceptron: Training and Optimization
Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.
Neural Networks for NLP
Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Neural Networks: Basics and Applications
Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.
The Hidden Convex Optimization Landscape of Deep Neural Networks
Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.
Deep Learning: Data Representations and Neural Networks
Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.
Neural Networks: Perceptron Model and Backpropagation Algorithm
Covers the perceptron model and backpropagation algorithm in neural networks.
Neural Networks: Training and Optimization
Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.
Neural Networks: Perceptron and Backpropagation
Covers the basics of neural networks, including the perceptron model and backpropagation.
Deep Learning: Convolutional Neural Networks and Training Techniques
Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Dynamics of Linear Neural Networks
Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.
Neural Networks: Regression and Classification
Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
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