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Business of Machine Learning

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The role of Machine Learning (ML) in business is an interesting topic. ML provides a powerful tool set for understanding data, but that is not enough. A fundamental tenet of strategy is differentiation, or finding a sustainable competitive advantage. We shall try to clarify basic alternatives below. Suunto dive computers use mathematical model for nitrogen saturation of human body. For deep dives or challenging conditions, using multiple computers provides safety. Exclusive access to data This is the most talked about approach at the moment: a company has exclusive access to data and performs value adding operations in them. In the simplest case, the data in question are related to normal operations of the company, and may be Customer Relationship Management (CRM) data or enterprise business data, for example. Machine learning can be used to automate business processes using data. A second type of exclusive data relates to a service product the company is offering. It may b
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Latency - the new black?

In this posting, we'll explore why latency matters. We shall proceed to explain why edge cloud architectures such as Multi-access Edge Computing (MEC) are important enablers for low latency and why that matters. Mobile data Not so long ago, this explanation would have been based on personal computers. Nowadays, folks use mostly handsets so let's talk about mobile data. I have been using mobile data all the way from circuit-switched (CS) data in GSM to GPRS/EDGE to UMTS to LTE. The first data-enabled device was the legendary Nokia Communicator, complete with tiny keyboard and e-mail application. At the moment I'm using Nokia 6 for work and iPhone 8 for personal use. Things have gotten better quite a bit better during the last twenty years. The most obvious improvement for laypersons is the impressive reduction in the delay with which content is opened in the handset. It is affected by both an improvement in the processing capacity of the handset and the data transfe
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AI in addition to ML?

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Well, topic is techno-ish, isn't it? I've been involved in training people within the company to apply deep learning in their work. It is truly delightful to see talented people quickly grasp the concepts and tools and proceed to create very nice applications quite soon afterwards. There is something special about deep learning which adds spark to the eyes of the cognoscenti. Having done my Ph.D. in computational physics, I was at first surprised by the attitude about this particular class of algorithms, both that of others as well as my own. I remember feeling child-like joy seeing a sequence model producing a good time series prediction for the first time. I also remember feeling rather less enthusiastic about some other algorithms during the Ph.D. research. Way back, if I may add. There are t heoretical reasons why deep learning is a special case. Most likely also the general enthusiasm about the discipline adds to one's excitement. Tooling such as Spyder IDE and
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Ethics and machine learning

Machine learning has many new aspects that need to be understood by the society since it is expected to be widely used in the society. A variant of machine learning called supervised learning is used for applications like object detection in pictures as well as for classification. Let's take a quick look at some of the basic concepts below. Model Model defines how input data is processed. Depending on the use case, input data may consist of digital pictures, digitized sounds, or numerical data, for example. The model consists of mathematical operations which transform the input data into desired output (e.g. classification "cat" / "dog" in picture). There are many algorithms which can be used for the model, ranging from simple mathematical formulae to fashionable deep learning networks. A model is basically neutral and generic, so that an individual model may be used for classifying cats vs. dogs on the one hand, or types of trees on the other. For dema
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On y va

Getting started with TensorFlow is relatively easy, assuming one is familiar with ANNs. TensorFlow's website  provides good examples to get one started. For an example on how ANNs are implemented with TensorFlow, go straight for "Get started" => "Deep MNIST for experts". Installation of TensorFlow on a computer without GPU support is easy, following the instructions on the website. Server installation in virtualenv, Anaconda installation using own package management system. One should note that  server installation is not visible in Anaconda if run on same system, one needs separate installs in this case. Below, I'll describe my experiences with GPU (GeForce GTX 1070 on Ubuntu 16.04 LTS) as well as notes about running TensorFlow based programs with both GPU and CPU. GPU installation Installation of TensorFlow with GPU support is a bit trickier, primarily because TensorFlow relies on NVIDIA CUDA  library. This in itself is not a problem, the challe
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Intro

This blog is about using Tensorflow with Python for machine learning. I'll start with giving some background information in order for the reader to better relate to what's coming up. I'll try to keep things short and to the point. Academic background & work history I have Ph.D. in computational materials physics and in the course of my studies, did numerical simulations about fracture of disordered media (paper specifically). The purpose of the research was to gain insight into fracture of paper so that paper machines could be run at higher speeds. I started my simulations with Cray X-MP and Convex computers which had vector processors inside. Programming was easy since Cray's compiler output vectorized code, provided that usual design criteria for vectorization were met with. In the middle of the graduate studies, computation platform changed to Cray T3E , which consisted of separate computer systems connected with fast optical fibre communications
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