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.
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.
Tim can be contacted on Twitter @timothy_hughes where he has some 175,000 followers or firstname.lastname@example.org 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.
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.
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.
This article on an introduction to implementing neural networks using TensorFlow, was posted by Faizan Shaikh. Faizan is a Data Science enthusiast and a Deep learning rookie. Sc. undergrad, he aims to utilize his skills to push the boundaries of AI research. If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can.
The recent explosion of interest in data science, data mining, and related disciplines has been mirrored by an explosion in book titles on these same topics. One of the best ways to decide which books could be useful for your career is to look at which books others are reading. This post details the 10 most popular titles in Amazon's Artificial Intelligence & Machine Learning Books category as of Nov 24, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers. Note: KDnuggets gets absolutely no royalties from Amazon - this list is presented only to help our readers evaluate interesting books. "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject."
Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic.
Machine learning is everywhere in the world of cybersecurity these days. It is often thought of as the magic bullet to secure systems and networks -- a tool able to identify previously invisible attacks through a nontransparent set of functions, as in neural nets. Transparency aside, neural nets and other algorithms have indeed proven very effective. Security professionals run into a distinct problem when attempting to do this, however. Machine learning classifiers perform much better in the supervised case, where labeled data is available.
A brief history of Machine learning • Most of the machine learning methods are based on supervised learning Input Feature Representation Learning Algorithm 10. Traditional machine perception • Hand crafted feature extractors 14. Human Brain Auditory Cortex Auditory cortex learns to see. Neural Network • Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. After processing the data, they send their output to the first hidden layer.