Deep Learning Building Blocks

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Related lectures (31)

Page 3 of 4

Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.

Regularized Cross-Entropy Risk

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Deep Learning: Convolutional Neural Networks

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Mathematics of Data: From Theory to Computation

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Multilayer Perceptron: Training and Optimization

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Recurrent Neural Networks: Neural Language Models

Explores fixed-context neural language models, RNNs, vanishing gradients, and sequence labeling in NLP.

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.