Artificial Intelligence Trends

Understanding The Artificial Intelligence Trends in 2018

The level of technological advancement and adoption of Artificial Intelligence in different business industries is incredible. Every firm is striving to integrate machine learning...

Important Unanswered Questions of 2018 in Data Science and Machine Learning

While the big data market is expected to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021, the artificial intelligence...

The Best Programming Languages To Learn For AI

Machine learning and artificial intelligence remain a branch of engineering that is ongoing and extremely in demand. With so many people attempting to build...

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...

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...

Neural Networks: Advantages and Applications

A human brain is not capable of solving complex data and cannot extract information from compound structures. To overcome this lack of...

Algorithms Every Machine Learning Expert Should Know

This post is all about the most popular machine learning algorithms. Before we start regardless of field, it advisable to list all available tools and techniques in...

5 Deep Learning Breakthroughs You Should Know

Deep learning and its many breakthroughs seem to be all over the news these days. Whether you are an individual developer or a practitioner...

Netflix just open-sourced Polynote, a cool Machine Learning, and Data Science workflow tool

Netflix just announced the open-source launch of Polynote, a new Polyglot notebook type that supports multiple languages, including Python, with Apache Spark integration, first-class Scala...

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...