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) …
Tensors are the primary data structures used by neural networks. And they are rather fascinating as well. Machine learning and by extension deep learning is an interdisciplinary field. Its interesting to note how many different people from many different fields came to same concepts. The concept of tensor is a mathematical generalization of more specific concepts, vectors and matrices in particular. In neural networks transformations, input, output etc are performed via tensors.
This is the post number 20 in the "Deep Reinforcement Learning Explained" series devoted to Reinforcement Learning frameworks. So far, in previous posts, we have been looking at a basic representation of the corpus of RL algorithms (although we have skipped several) that have been relatively easy to program. But from now on, we need to consider both the scale and complexity of the RL algorithms. In this scenario, programming a Reinforcement Learning implementation from scratch can become tedious work with a high risk of programming errors. To address this, the RL community began to build frameworks and libraries to simplify the development of RL algorithms, both by creating new pieces and especially by involving the combination of various algorithmic components.
The remote workplace has ended more than just rolling your chair over to your favorite coworker's desk. Interaction levels with HR have "basically fallen off a cliff," says Robert Toole, a partner at Kona HR Consulting. Large corps often have internal HR tech tools, but many small and medium firms are adopting them for the first time during Covid-19. Historically, HR's go-to employee pulse-check has been a lengthy annual survey. But in these…less-than-certain times, employees' lives are changing too quickly for a yearly questionnaire.
For the last year and a half I have been using Watson Studio and DSX Local as my development environments for exploring machine learning and implementing models. Inspired by Siraj Raval's video on Azure Machine Learning I decided to take the plunge and check out Microsoft's ML environment. This posting covers my first impressions, the good and the bad, and contrasts Azure ML with Watson Studio / DSX. Getting a first taste of Azure ML from a standing start is relatively easy -- here's a quick overview of the preparatory steps: As Siraj notes in his video, Microsoft touts a hybrid (on prem cloud) approach. Before getting into the experience of using Azure ML, I'd like to contrast my experience of "hybrid" Microsoft vs. IBM: Full disclosure: I am an IBM employee, but I can see pros and cons to both approaches.
The insurance industry is seeing a welcome disruption via artificial intelligence (AI), but only a few companies might benefit from this breakthrough. Most organizations lack cognitive technologies to process insight, and this makes the data almost useless. But insurtech companies can connect the potential of the AI data streams available. In this complete introduction to artificial intelligence, you'll be learning: And although artificial intelligence is massively popular, other complex tech topics like big data and deep learning can often cause confusion. So if you want to leverage AI and get the best out of this breakthrough, this article is for you.
As I pledged in my last article that I would be writing about algorithms in next article. Algorithms are the core to building machine learning models and here I am providing details about most of the algorithms used for supervised learning to provide you with intuitive understanding for where to use it and where not to. By the end of this article, you will be adept at algorithms from intuitive level of understanding. So, folks here we go. Naive Bayes are the algorithms used for classification based on Bayes theorem and it is the foundational algorithm to know at most for machine learning.