Logistic Regression With A Real-World Example in Python
In this tutorial, you will learn Python Logistic Regression. Here you’ll know what exactly is Logistic Regression and you'll also see an Example with...
How DoorDash Uses Machine Learning ML And Optimization Models To Solve...
DoorDash is a logistic platform that delivers millions of orders every day with the help of its DeepRed system. In their recent blog, Data...
NVIDIA Base Command Platform Allows Enterprises To Move Their AI Projects...
Recently, NVIDIA launched its “NVIDIA Base Command,” a comprehensive platform that manages the end-to-end lifecycle of AI development and reporting dashboards. It is a...
Getting Started With Pytorch In Google Collab With Free GPU
In this post, we will discuss the basics of pytorch by using the google colab gpu
Implementing Batching for Seq2Seq Models in Pytorch
In this tutorial, we will discuss how to implement the batching in sequence2sequene models using Pytorch. We will implement batching by building a Recurrent...
Classifying the Name Nationality of a Person using LSTM and Pytorch
Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. In the case of Convolution Neural Networks (CNN), the output from the softmax layer in the context of image classification is entirely independent of the previous input image.
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 is the...
How This Data Startup Aims to Bring Access to Petabytes of...
After four years of hard work by Kevin Moore and Aneesh Karve (Founders), Quilt Data comes live from stealth mode. This platform can be used to...
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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.