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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

How to Connect Google Colab with Google Drive

In this tutorial, you’ll learn how to connect your Google Colab with Google Drive to build some Deep Learning model on Google...

Exploratory Data Analysis Tutorial: Analyzing the Food Culture of Bangalore

In this blog post, we will discuss how to perform exploratory data analysis by creating awesome visualizations using matplotlib and seaborn by taking a real-world data set.

Building a Feedforward Neural Network using Pytorch NN Module

Feedforward neural networks are also known as Multi-layered Network of Neurons (MLN). These network of models are called feedforward because the information only...

5 Must Excel Add Ins​​ For Data Science Practice

Below is the list of five important Data Science practice add-ins used in Microsoft Excel. 1. Power Pivot: Power Pivot...

List of Data Science and Machine Learning GitHub Repositories to Try in 2019

Here is the list of selected Data Science and Machine Learning GitHub Repositories to Try in 2019 Paper with...

21 Free Data Sets/ Projects for Data Science Beginners

We have listed below some of the best datasets/ projects for data science beginners. 1. Enron Emails: 

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.

Logistic Regression With A Real-World Example in Python

In this tutorial, You’ll learn Logistic Regression. Here you’ll know what exactly is Logistic Regression and you'll also see an Example...

Deep Learning with Keras – Part 5: Convolutional Neural Networks

Motivation In the previous articles, we solved problems with numeric and categorical data, and we learned the different transformations...