In this tutorial, we are going to take a look at how to get started with TensorFlow on CentOS. We will be covering two methods. First, we will take a look at installing TensorFlow in a Python virtual environment via the Python package manager pip. After that, we will walk through installing TensorFlow via the Anaconda package manager. Finally, we will cover building a TensorFlow pip package from source.Continue reading “How to Install TensorFlow on CentOS”
In this tutorial, we are going to cover how to set up a Python virtual environment on CentOS. A Python virtual environment makes it possible to install Python packages into a discreet Python ecosystem that is entirely separate from your system’s default Python framework. This means that you do not have to worry about overwriting the installation of any current packages that might be defaulted to the existing version of Python on your system.Continue reading “How to Setup a Python Virtual Environment on CentOS”
We here at Liquid Web know how important good solid information can be. We also know that we have some of the most intelligent customers on the planet. With this in mind, we opt to try and ensure that you are kept up to date on the latest tech and information. It is with this in mind we continue to offer the latest knowledge available regarding ways to improve your service, upgrade your ability to work with your server(s), and enhance your overall effectiveness in growing your business.Continue reading “What Is Python?”
In this tutorial, we are going to walk through how to install scikit-learn on an Ubuntu 18.04 server. We are going to walk through the installation both in a virtual environment with the Python package manager, Pip, and via Anaconda.Continue reading “How to Install Scikit-Learn on Ubuntu 18.04”
Data analysis via machine learning is becoming increasingly important in the modern world. PyTorch is a machine learning Python library, developed by the Facebook AI research group, that acts as a high-level interface for developers to create applications like natural language processors. In this tutorial, we are going to cover how to install PyTorch via Anaconda and PIP.Continue reading “How to Install PyTorch on Ubuntu”
In this tutorial, we are going to set up TensorFlow in a virtual Python environment on Ubuntu 18.04. TensorFlow is an open-source framework, developed by the Google Brain team, designed to be a high-level interface for implementing machine learning and mathematical operations. This library provides developers an avenue to work on complex projects like neural networks through an easy to use Python API. One of the significant benefits of having a Python front-end is that it is portable between operating systems like Linux and Windows.Continue reading “How To Install TensorFlow on Ubuntu 18.04”
Whether you’re a beginner or a professional, TensorFlow is an end-to-end platform that makes building and deploying Machine Learning models a snap! Because TensorFlow is based on the Python system, you can install it on multiple operating systems, including Windows. This article will take you through the necessary steps to get TensorFlow installed on your Windows server.Continue reading “Install TensorFlow on Windows”
Reading Time: < 1 minute Continue reading “What Can Machine Learning Do? [Infographic]”
Reading Time: 3 minutesIt was 2017 when American businessman Mark Cuban said that if you don’t understand artificial intelligence, deep learning and machine learning “you’ll be a dinosaur within three years.” Time will tell as to whether he is right, but if his theory has substance, some companies are well into the 12-month countdown of becoming extinct.
What is Machine Learning?
In its purest form, machine learning teaches computers to learn in the same way that humans do. It collects and interprets data from the world around us and makes decisions on what to do with that information. Machine learning is one of the first applications of artificial intelligence.
Just think about every time you start a search using Google. How can it find all the relevant matches to your terms? Considering there are 30 trillion unique web pages that search engines trawl to retrieve what you need, it is even more impressive. It’s impossible for a human to explore that many pages in a lifetime. This is the essence of machine learning, without intervention computers learn to use data to accomplish human tasks in a fraction of the time.
Machine Learning and Data
It is almost impossible to stress just how vital data is to machine learning; in fact, they are just about synonymous with each other. This is probably best summarised within the Data Science Hierarchy of Needs penned by Rogati, 2017.
At the top of the hierarchy is the AI or Deep Learning algorithm. This might be the algorithm that recommenders which Netflix show to watch or Amazon Alexa responding to your voice command. However, at the very start of the journey is data collection and the quality of what feeds the algorithm.
As an example, marketing teams use machine learning applications to hyper-personalize communications. This is why we tend to get emails or notifications that are highly relevant and tailored to our needs. The machine has studied our data and knows exactly what we need and when we want it. Had the initial data been incorrect or “dirty” in any way, customers would receive communications that are not relevant. What if somebody had accidentally entered a customer location as the U.K. on an order form instead of the U.S. and all pricing is calculated pounds instead of dollars? The customer would soon unsubscribe to an email list because it doesn’t pertain to them.
A company can have the best algorithms in the industry, but without quality data, they are effectively useless and possibly detrimental. To counter these problems, companies deploying machine learning technology will usually start by designing a data quality or governance strategy which negates the risk. Adopting AI is a journey and must begin with getting the simple things right.
Machine Learning Framework
Hiring a team to design and deploy machine learning applications can be costly. While Data Scientists are usually specialists in statistical methods and incredibly adept with coding languages like Python and R; they often find it hard to present findings to Data Analysts or Insight Managers. However, the algorithms also need to be deployed onto platforms requiring a Data Engineer or Developer. There also needs to be duplicate roles to avoid single points of failure, and of course, everybody needs powerful processors that can analyze vast amounts of data. Suddenly, one Data Scientist has become a team of 8 people with expensive hardware and costs have escalated!
The role of machine learning has been growing exponentially in the last few years, and it looks set to continue with recent developments in cloud, edge and quantum computing which will only increase the potential processing power. Companies who fail to realize the capability of AI will fall behind the competition.
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