Concept

Convolutional neural network

Related lectures (30)

Convolutional Networks: Overview and Architecture

Covers the motivation and architecture of convolutional networks, from LeNet to AlexNet.

Convolutional Neural Networks

Explores Convolutional Neural Networks, focusing on layers, filters, pooling, and weight sharing.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Neural Networks: Multilayer Learning

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

Convolutional Neural Networks

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Deep Learning: Convolutional Neural Networks

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

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Convolutional Neural Networks: Fundamentals

Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

Convolutional Networks: Motivation & Ideas

Explores the motivation and ideas behind convolutional networks, emphasizing weight sharing and pooling layers.

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 Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Convolutional Neural Networks

Introduces Convolutional Neural Networks, covering fully connected layers, convolutions, pooling, PyTorch translations, and applications like hand pose estimation and tubularity estimation.

Convolutional Neural Networks

Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.

Deep Learning for Autonomous Vehicles: Learning

Explores learning in deep learning for autonomous vehicles, covering predictive models, RNN, ImageNet, and transfer learning.

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Nonlinear Supervised Learning

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

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Recurrent Neural Networks: Language Detection

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Pavement Distress Detection

Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.