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Concept

PyTorch

Related lectures (23)

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

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.

Pytorch Intro: MNIST and Digits

Covers Pytorch basics with MNIST and Digits datasets, focusing on training neural networks for handwritten digit recognition.

Deep Learning Building Blocks: Linear Layers

Explains the fundamental building blocks of deep learning, focusing on linear layers and activation functions.

Deep Learning Building Blocks

Covers the fundamental building blocks of deep learning, including tensors, backpropagation, and PyTorch.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.

Binary Sentiment Classifier Training

Covers the training of a binary sentiment classifier using an RNN.

Kernel Methods: Neural Networks

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

Document Segmentation: dhSegment

Explores the development and improvements of dhSegment, an open-source document segmentation package using PyTorch.

Convolutional Neural Networks

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

Crash course on Deep Learning

Covers a crash course on deep learning, including the Mark I Perceptron, neural networks, optimization algorithms, and practical training aspects.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

Learning with Deep Neural Networks

Explores the success and challenges of deep learning, including overfitting, generalization, and the impact on various domains.

Model Finetuning for NLI

Covers the second assignment for the CS-552: Modern NLP course, focusing on transfer learning and data augmentation.

Deep Neural Networks: Optimization and Approximation

Explores optimization and approximation in deep neural networks, including optimal control and numerical experiments.

Training Neural Networks

Covers the training of neural networks using stochastic gradient descent, chain rules, gradients computation, weight decay, and dropout.

PyTorch and Convolutional Networks Log in to Mediaspace to watch this video

Covers PyTorch tensor data structure and training a CNN to classify images.

Deep Learning: Convolutional Networks Log in to Mediaspace to watch this video

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

Fully Connected Networks on MNIST and SUSY Datasets Log in to Mediaspace to watch this video

Covers the implementation of fully connected neural networks on two datasets using PyTorch.

Neural Network Approximation and Learning Log in to Mediaspace to watch this video

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.