INTRODUCTION


Tensorflow Tutorial: Part 1 – Introduction

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This series is excerpts from a Webinar tutorial series I have conducted as part of the United Network of Professionals. Many applications as of today have tensorflow embedded as part of their machine learning applications. Let's explore the tensorflow environment and how the flexible architecture makes implementation so easy. This means you can execute code locally in your laptop with a CPU of a GPU if you have one.


Watson IoT Presenting Cognitive Coffee

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IBM Watson Internet of Things 2,130 views Cognitive Manufacturing with Watson IoT - Duration: 4:15. IBM Watson Internet of Things 6,978 views Watson IoT: Presenting Cognitive Coffee - Duration: 4:56. IBM Watson Internet of Things 11,228 views IBM CIO Leadership Exchange - Cognitive IoT: Where Digital Meets Physical - Duration: 42:55. IBM Watson Internet of Things 721 views Your brain on video games Daphne Bavelier - Duration: 17:58.


Is Artificial Intelligence (AI) the New Sales Frontier?

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Tim can be contacted on Twitter @timothy_hughes where he has some 175,000 followers or tim@social-experts.net Digital Leadership Associates is an agency to help companies move to digital and social. We help you to achieve your key business goals through three unique programs; Social Strategy definition and implementation, Social Selling training and mentoring and Social Presence management. We will help you define a Social Media strategy and whole-business understanding of your social vision, we equip your sales team with the tools and knowledge to become skilled at Social Selling and we offer deliver partial/whole management of your social presence. DLA provides advice and guidance to companies, given by actual Social practitioners, that is people with actual experience in social media, social and digital transformations.


5 EBooks to Read Before Getting into A Machine Learning Career

@machinelearnbot

Note that, while there are numerous machine learning ebooks available for free online, including many which are very well-known, I have opted to move past these "regulars" and seek out lesser-known and more niche options for readers. The book has wide coverage of probabilistic machine learning, including discrete graphical models, Markov decision processes, latent variable models, Gaussian process, stochastic and deterministic inference, among others. The material is excellent for advanced undergraduate or introductory graduate course in graphical models, or probabilistic machine learning. One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research.


Introduction to Machine Learning for Developers

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The two main types of machine learning algorithms are supervised and unsupervised learning. There are many types of supervised algorithms available, one of the most popular ones is the Naive Bayes model which is often a good starting point for developers since it's fairly easy to understand the underlying probabilistic model and easy to execute. Decision trees are also a predictive model and have two types of trees: regression (which take continuous values) and classification models (which take finite values) and use a divide and conquer strategy that recursively separates the data to generate the tree. Check out the rest of the blog for more resources on natural language processing and machine learning algorithms such as LDA for text classification or increasing the accuracy on a Nudity Detection algorithm and a beginners tutorial on using Scikit-learn to solve FizzBuzz.


Introduction to algorithms, machine learning and AI

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As the field of artificial intelligence grows and enters the marketing mainstream it brings with it a host of new vocabulary and subtlety. At Adweek, a panel on AI declared it and machine learning to be the same thing for the purposes of their discussion. But while the terms might be interchangeable from an event perspective, they equate to very different things. As the fields of machine learning and AI become more entwined in our rhetoric it's important we have a clear understanding of the difference – the last thing the industry needs is another "native", where definitions vary wildly from one provider to another. An algorithm is a series of instructions written by a programmer for software to follow.


The End of Human Doctors – Introduction

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I have emerged, blinking, from the darkness of grant/paper writing purgatory (a.k.a December to March in Australia). It is time to get the blog going again, and to make up for the long gap in posts I'm going to start with the big one. The question I get every time I tell a colleague what I am working on, every time I give a lecture, every time I chat with someone new on social media. Over the course of the coming few blogposts, I intend to give my best answer to that question. I hope I can do it justice, because I don't really think it has been adequately explored elsewhere.


A Seemingly Simple Tweak Unlocks Google Home's Full Potential

WIRED

Voice-powered speakers like Amazon Echo and Google Home have carved out a place on kitchen counters and nightstands in countless homes. What makes their immense popularity all the more remarkable is that they've achieved it without a key feature: Knowing exactly who's talking. Starting today, your Google Home device will be able to identify up to six distinct voices. Starting today, your Google Home device can identify as many as six voices, and summon information based on each person's calendars, services, and preferences. In doing so, Google's speaker-bound personal assistant becomes truly personal, unlocking the true potential of the most promising new category of consumer tech to come along in years.


The InfoQ eMag: Introduction to Machine Learning

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Machine learning has long powered many products we interact with daily--from "intelligent" assistants like Apple's Siri and Google Now, to recommendation engines like Amazon's that suggest new products to buy, to the ad ranking systems used by Google and Facebook. More recently, machine learning has entered the public consciousness because of advances in "deep learning"--these include AlphaGo's defeat of Go grandmaster Lee Sedol and impressive new products around image recognition and machine translation. While much of the press around machine learning has focused on achievements that were not previously possible, the full range of machine learning methods--from traditional techniques that have been around for decades to more recent approaches with neural networks--can be deployed to solve many important (but perhaps more prosaic) problems that businesses face. Examples of these applications include, but are by no means limited to, fraud prevention, time-series forecasting, and spam detection. InfoQ has curated a series of articles for this introduction to machine learning eMagazine covering everything from the very basics of machine learning (what are typical classifiers and how do you measure their performance?), to production considerations (how do you deal with changing patterns in data after you've deployed your model?), to newer techniques in deep learning.


Introduction to Scikit Flow - Yuan's Blog

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In November, 2015, Google open-sourced its numerical computation library called TensorFlow using data flow graphs. Its flexible implementation and architecture enables you to focus on building the computation graph and deploy the model with little efforts on heterogeous platforms such as mobile devices, hundreds of machines, or thousands of computational devices. TensorFlow is generally very straightforward to use in a sense that most of the researchers in the research area without experience of using this library could understand what's happening behind the code blocks. TensorFlow provides a good backbone for building different shapes of machine learning applications. However, there's a large number of potential users, including some researchers, data scientists, and students who may be familiar with many data science concepts/algorithms already but who never get involved in deep learning research/applications, may found it really hard to start hacking.