Classify Handwritten-Digits With Tensorflow
One of the capabilities of deep learning is image recognition, The “hello world” of object recognition for machine learning and deep learning...
AttoNets, A New AI That is Faster & Efficient For Edge Computing
An AI team at the University of Waterloo, Canada, developed a new type of compact family of deep neural networks (AttoNets), which...
How This Data Startup Aims to Bring Access to Petabytes of Public Data &...
After four years of hard work by Kevin Moore and Aneesh Karve (Founders), Quilt Data comes live from stealth mode. This platform can be...
Deep Learning with Keras – Part 7: Recurrent Neural Networks
Intro
In this part of the series, we will introduce Recurrent Neural Networks aka RNNs that made a major...
AI and Data Science Tools on Amazon Web Services
As the leading cloud provider, Amazon Web Services offers numerous tools for a variety of applications. The sheer number of offerings can...
Neural Structured Learning: A new way for AI to learn.
Neural Structured Learning (NSL) is a new open source tool to train neural networks by using Neural Graph Learning, utilizing structured data...
How Artificial Intelligence Supports Human Intelligence in Business
In June, researchers at MIT and Brown University revealed their latest creation: Northstar. The system uses artificial intelligence (AI) and machine learning...
List of AI Conferences in 2019
ConferenceCountryDateDirect Link Biologically Inspired Cognitive Architectures (BICA) USA Aug 2019 Click here AI Summit by Xavier Health USA Aug...
Top Data Science Podcasts To Listen
SuperDataScience
Learning Machines 101
IBM Analytics Insights
Talking Machines
Introduction to Image Classification using Pytorch to Classify FashionMNIST Dataset
In this blog post, we will discuss how to build a Convolution Neural Network that can classify Fashion MNIST data using Pytorch on Google Colaboratory. The way we do that is, first we will download the data using Pytorch DataLoader class and then we will use LeNet-5 architecture to build our model. Finally, we will train our model on GPU and evaluate it on the test data.
