Convolutional Neural Networks: Visualize the output of layers that make up a CNN.
Introduction to PyTorch: Learn how to build neural networks in PyTorch and use pre-trained networks for state-of-the-art image classifiers.
Sentiment Analysis with NumPy: Andrew Trask leads you through building a sentiment analysis model, predicting if some text is positive or negative.
Introduction to Neural Networks: Learn how to implement gradient descent and apply it to predicting patterns in student admissions data.
Table Of Contents Tutorials Introduction to Neural Networks In the program itself, the projects are reviewed by real people (Udacity reviewers), but the starting code is available here, as well. There are also notebooks used as projects for the Nanodegree program. There are other topics covered such as weight initialization and batch normalization. In most cases, the notebooks lead you through implementing models such as convolutional networks, recurrent networks, and GANs. It consists of a bunch of tutorial notebooks for various deep learning topics. This repository contains material related to Udacity's Deep Learning v7 Nanodegree program.
0 kommentar(er)
