Many of the most exciting high-tech projects nowadays include bringing together knowledge from two or more well-established and fast-growing fields. One of the prominent examples is applying machine learning in order to filter and analyze the huge amount of data we obtain from the Internet of Things (IoT). But first, let’s see why IoT needs help from artificial intelligence in order to reach its full potential.
The open source machine learning frameworks just keep on coming. Oryx 2 is focused on real-time, large scale machine learning and uses the power of 3 tiers. Grab it by the horns and create custom applications.
Machine learning enables customized conversations between man and machine that can result in buying decisions. We asked Tina Nord and Kathleen Jaedtke to explain how this can be achieved through the use of dialogue-oriented technologies. Let’s take a look at how communication between man and machines works.
It’s time to take another pick at what’s going on in the developer world! The 15th edition of the Developer Economics: State of the Developer Nation report has been released and it has some interesting insights into current developer trends.
Artificial Intelligence and Machine Learning our one of the hottest topics out there and not only in the programming world. Packt surveyed more 2000 professionals to find out what is the developers’ perspective on AI. Let’s have a closer look at some of the most interesting results.
Machine learning’s growth continues as it permeates into unrelated industries. Travel booking might not seem like a good fit at first, but Wilco van Duinkerken of trivago explains how ML is innovating the way you find and book your next holiday.
Did you know you can count bees with AI? We take a look at some real world use cases for machine learning that you might have missed.
Are you ready for machine learning? Do you have a plan? In this article, Atakan Cetinsoy from BigML goes over six things that every organization needs to be aware of when they’re devising their own machine learning strategy.
Prepare to be dazzled by what this small team of academics managed to achieve. Fast.ai is an organization that aspires to make machine learning accessible to everyone. Their most crowning achievement? Well, not much. Just outperforming the AI giant of Google!
How can developers learn to utilize machine learning in their DevOps practice? In this article, Prasanthi Korada goes over some basic approaches that can help developers apply cutting edge tech like machine learning to their everyday work.
There are a number of issues that arise when data scientists and ML researchers meet DevOps to try to deploy, audit, and maintain state-of-the-art AI models in a production and commercial environment. Peter Zhokhov and Arshak Navruzyan discuss a new open source software tool called Studio.ML, which offers a number of solutions to this problem.
With artificial intelligence and machine learning becoming increasingly relevant for modern enterprises, many companies might be feeling the pressure to invest in an AI strategy, before fully understanding what they are aiming to achieve. In this session, Sumanas Sarma and Rob Hinds explain how you can go from theory to production in adopting machine learning solutions.
Amazon has been using predictive models for decades. Now, they want to put machine learning in the hands of every developer with their AWS AI division. In this article, Cyrus Vahid, an AI specialist from AWS, explains some basic models for deep learning and goes over how Amazon has a service for every use case.
TensorFlow 1.9 is here! So what does this latest update mean for the popular machine learning project? For starters, there’s an improved tf.keras beginner’s guide. For everyone else, there’s eager execution, improved GRU and LSTM implementation, and gradient boosted trees estimators.
