If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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.
A few months, we took an early look at running Keras with Apache MXNet as its backend. Keras supports multiple backends for training and it's very easy to switch from one to the other. After a while, here's the result (full log here). Here's the result after 100 epochs (full log here): 43 minutes, 99.4% training accuracy, 62% test accuracy.
Laurent is a Senior Research Associate at the University of Cambridge, where his work focuses on the development and application of machine learning methods to understand high throughput biological data. Alternatively, Barbara Fusinska will be doing an all-day session on Practical Deep Learning with TensorFlow. Using an interactive learning platform, attendees will have a practical opportunity to use TensorFlow when building deep networks, training them and evaluating the results. After two days of conference sessions, either workshop is an excellent opportunity to dive deep on the fundamentals of machine learning and deep networks.
It is a machine-learning library using data flow graphs to build models. TensorFlow has been created for Deep Learning to let a user create a neural network architecture by himself (or herself, of course). Actually, tensors flow in the graph from node to node, thus making the name of the library sound logical. For some of you it may be interesting if there is any difference between TensorFlow and libraries like Theano, which also can make their own Deep Learning with multi-dimensional arrays and GPU.
It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming. Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. Machine Learning for Language Toolkit (MALLET) is a Java toolkit fro statistical natural language processing, document classification, clustering, topic modeling and information extraction.
Google wants to spread the deep learning to more developers, so it has unveiled a mobile AI vision model called MobileNets. The tech is part of TensorFlow, Google's deep learning model that recently shrunk down to mobile size in a new version called TensorFlow Lite. The larger the model, the better it is at recognizing landmarks, faces or doggos, with the most CPU-intensive ones hitting scores of between 70.7 and 89.5 percent accuracy. Those aren't far from Google's cloud-based AI, which can recognize and caption objects with around 94 percent accuracy, last we checked.
The heart of this offering is Google's machine learning software TensorFlow. He says his team uses a range of machine learning frameworks to create in-house tools for tasks like categorizing customer feedback, but that TensorFlow is often a good place to start. "There are technical differences between [different AI frameworks], but machine learning communities live off community support and forums, and in that regard Google is winning," he tells The Verge. But Google didn't forget to feed the community either, and to complement these announcements unveiled new tools to help developers build AI services that work better on mobile devices.
Blazing the web for black-boxes to reverse-engineer, I came upon the following informative and down-to-earth article. I'm sharing it, as it hasn't found its way to the general public. The open-source AI trend represents the convergence of two trends: The open-source movement and the artificial intelligence revolution. The open-source movement is the trend toward making the source code for software available to developers and users so that anyone can modify it and, theoretically, improve it. Examples of open-source software include the Linux operating system, the Mozilla Firefox browser, and the Android mobile platform.
Django continues to be the pick of libraries for Python developers. However, there are some not-so-well-known libraries that happened to gain traction among Python developers in 2016. In this blog, I am unveiling 7 Python libraries, excluding the established ones like Django, Flask, etc, that Python developers may find worth considering in 2017. Mobile apps are everywhere, and are often meant for global population - be it for games, social media, health monitoring and whatnot. However, the problem with the standard data/time library for Python is that it doesn't meet the requirements of modern apps that have their target audience living in different regions and countries.