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) …
In the previous article in this series, "Diving into Machine Learning" we looked at some common approaches to machine learning, which is a subset of AI that provides systems with the ability to learn from data and improve over time without being explicitly programmed. In this latest article in our Enterprise AI series, we provide an overview of deep learning, which is a specific approach to the more general category of machine learning. As with other machine learning techniques, deep learning is an important building block for artificial intelligence in the enterprise. First, let's quickly review what machine learning is. Machine learning refers to the process of training a model, which is nothing more than a function that maps inputs (e.g., house size, customer preferences) to outputs (e.g., house value, new product recommendations).
In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning. This tutorial will serve as an introduction to the concept of working with deep learning in a temporal nature, paving the way for when we discuss Long Short-term Memory networks (LSTMs) and eventually human activity recognition. To learn how to perform video classification with Keras and Deep learning, just keep reading! Videos can be understood as a series of individual images; and therefore, many deep learning practitioners would be quick to treat video classification as performing image classification a total of N times, where N is the total number of frames in a video. Video classification is more than just simple image classification -- with video we can typically make the assumption that subsequent frames in a video are correlated with respect to their semantic contents.
As a person coming from .NET world, it was quite hard to get into machine learning right away. One of the main reasons was the fact that I couldn't start Visual Studio and try out these new things in the technologies I am proficient with. I had to solve another obstacle and learn other programming languages more fitting for the job like Python and R. You can imagine my happiness when more than a year ago, Microsoft announced that as a part of .NET Core 3, a new feature will be available – ML.NET. In fact it made me so happy that this is the third time I write similar guide. Basically, I wrote one when ML.NET was a version 0.2 and one when it was version 0.10. Both times, guys from Microsoft decided to modify the API and make my articles obsolete. That is why I have to do it once again.
Significant advances are being made in artificial intelligence, but accessing and taking advantage of the machine learning systems making these developments possible can be challenging, especially for those with limited resources. These systems tend to be highly centralized, their predictions are often sold on a per-query basis, and the datasets required to train them are generally proprietary and expensive to create on their own. Additionally, published models run the risk of becoming outdated if new data isn't regularly provided to retrain them. We envision a slightly different paradigm, one in which people will be able to easily and cost-effectively run machine learning models with technology they already have, such as browsers and apps on their phones and other devices. Through this new framework, participants can collaboratively and continually train and maintain models, as well as build datasets, on public blockchains, where models are generally free to use for evaluating predictions.
Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow colour hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.
What are the differences between econometrics, statistics, and machine learning? I discovered this myself a couple years ago, through an analysis of the economics literature that required the research team to classify articles into economics fields (like labor and macro) and research styles (like theory and econometrics). The project was motivated by frustration with complaints lodged against academic economics in the wake of the Great Recession (perhaps you've seen the movie version: Inside Job). I thought: "What's with all the whining? "Economics has never been better!"
Over the last 15 years there has been a surge in the use of machine learning to gain materials chemistry insights. These methods use existing data (largely computed with ab-initio methods) to train statistical models that can make useful predictions about whether chemical compounds will be stable, and the properties they are likely to exhibit. However, a large majority of the knowledge the scientific community has generated to date is recorded as "unstructured" text, and has therefore been largely inaccessible to machine-learning and statistical analysis. In recent years however, the Natural Language Processing (NLP) research community has made great progress on methods to computationally parse and learn from unstructured text. In our paper, we show how the application of an unsupervised NLP model can capture information from the materials chemistry literature in a way that also uncovers latent knowledge previously unknown to the research community.
Last year we looked at'Relational inductive biases, deep learning, and graph networks,' where the authors made the case for deep learning with structured representations, which are naturally represented as graphs. Today's paper choice provides us with a broad sweep of the graph neural network landscape. It's a survey paper, so you'll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them. We'll be talking about graphs as defined by a tuple where is the set of nodes (vertices), is the set of edges, and A is the adjacency matrix. An edge is a pair, and the adjacency matrix is an (for N nodes) matrix where if nodes and are not directly connected by a edge, and some weight value 0 if they are.
Over the past decade or so, convolutional neural networks (CNNs) have proven to be very effective in tackling a variety of tasks, including natural language processing (NLP) tasks. NLP entails the use of computational techniques to analyze or synthesize language, both in written and spoken form. Researchers have successfully applied CNNs to several NLP tasks, including semantic parsing, search query retrieval and text classification. Typically, CNNs trained for text classification tasks process sentences on the word level, representing individual words as vectors. Although this approach might appear consistent with how humans process language, recent studies have shown that CNNs that process sentences on the character level can also achieve remarkable results.