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Machine Learning

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MARKETING DIGITALLY - Official Blog of Glenn Miller - Marketing Digitally, Digital Strategy and Online Marketing Speciaist. Machine Learning for Dummies: Part 1 – Chatbot’s Life. I often get asked on how to get started with Machine Learning.

Machine Learning for Dummies: Part 1 – Chatbot’s Life

Most of the time, people have troubles understanding the maths behind all things. And I have to admit, I don’t like the maths either. Math is an abstract way of describing things. And I think the way machine learning is described is too abstract to understand it easily. So in this article (series?) There will also be an experimental github repository where I put everything together, so that you can follow the steps and implement things on your own.

The first thing you have to know is that there are different concepts that allow different solutions. There are some different solutions, I’ll dig into the more complex solutions later when you’re ready for them. However, there are more complex architectures around from the engineering and biological perspective; and I think they’re a quite powerful toolset to know — as they can be freely combined with “low-level” machine learning solutions. 0. Neural networks are dumb. 1. Personalized offline ads - Part 1: Recognizing people. This post concerns Greenhouse Group Labs, an innovation program for students, established by Greenhouse Group.

Personalized offline ads - Part 1: Recognizing people

Labs is an ideal opportunity to test the latest technologies available, while allowing for talented young individuals to deeply explore them and come up with groundbreaking solutions. Online ads are getting more and more personalized. To a large degree, this is made possible because of the huge amounts of data gathered online. Using this data, smart algorithms are able to tailor ads to a specific person's interests and needs. What if we would be able to personalize ads on the same level in the offline world? Facial recognition. Index. The Difference Between AI, Machine Learning, and Deep Learning? This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.

The Difference Between AI, Machine Learning, and Deep Learning?

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to. For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both. From Bust to Boom Over the past few years AI has exploded, and especially since 2015. Good, but not mind-bendingly great.

Beginning Machine Learning with Keras and TensorFlow by thoughtram. This isn’t our typical kind of blog post.

Beginning Machine Learning with Keras and TensorFlow by thoughtram

In fact this one is very special. It’s the beginning of our journey with a new shiny toy. Every now and then there comes a field of technology that strikes us as being especially exciting. With all the latest accomplishments in the field of artificial intelligence it’s really hard not to get excited about AI. Companies such as Google, NVIDIA or are using neural networks to train cars that know how to drive themselves. We are happy to jump on this exciting journey and we are even happier to share our findings with you.

What’s all the buzz about AI and Machine Learning If you like to learn about the difference between AI, Machine Learning and Deep Learning we recommend this article by NVIDIA. So what exactly is exciting about AI? We are facing times where we tackle problems that seem to be too hard to solve with traditional programming techniques. With a fair amount of practice a human can drive a car. That’s the point of Machine Learning. Injecting intelligence – Building apps using Microsoft Cognitive Services. Technology it seems is moving ever faster and faster, especially in the world of Artificial Intelligence and Machine Learning.

Injecting intelligence – Building apps using Microsoft Cognitive Services

Barriers to entry are breaking down and huge cloud offerings from all the major suppliers are popping up left right and center. We see these on our devices (Siri, Cortana, Google Now), in our browsers yet to date, it’s being used mainly for one thing it seems, Ads (joy). So with the skill requirements at an all-time low, just about anyone can dive in and start making use of all the “intelligence” offerings that are available and start building the intelligent solutions for tomorrow. One of the big things that brought this to the forefront for me was the Re:Cognition event (hosted by Moov2 and Microsoft), which was a large hack-a-thon style event aiming to get teams crunching on Microsoft’s new “Cognitive Services” offering, which is an entire suite of API’s with a plethora of features to bring intelligence in to your apps & games.

And many many more! Injecting intelligence – Building apps using Microsoft Cognitive Services.


Evelina Gabasova on Machine Learning.