Concept

Highway network

Related lectures (31)

Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.

Synaptic Plasticity: Online Memory Learning

Explores synaptic plasticity, spike-timing models, and online memory learning challenges in computational neuroscience.

Transformers: Revolutionizing Attention Mechanisms in NLP

Covers the development of transformers and their impact on attention mechanisms in NLP.

Quantum State Tomography: Neural Network Approach

Explores a neural network approach to quantum state tomography using RBM, showcasing accurate predictions and potential applications beyond RBMs.

Convolutional Neural Networks: Structure & Regularization

Explores Convolutional Neural Networks, data augmentation, weight decay, and dropout for enhancing model performance.

Deep Learning: Backward Propagation and Vanishing Gradient

Delves into backward propagation in deep learning, addressing the vanishing gradient challenge and the need for effective hidden units.

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

Part-of-Speech Tagging: Probabilistic Models

Explores Part-of-Speech tagging using probabilistic models like Hidden Markov Models and discusses the resolution of lexical ambiguities.

Convolutional Neural Networks: Semantic Segmentation

Explores Convolutional Neural Networks for semantic segmentation, discussing models for pixel classification, learned decoding, and the importance of skip connections.