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) …
Sentiment analysis of free-text documents is a common task in the field of text mining. In sentiment analysis predefined sentiment labels, such as "positive" or "negative" are assigned to texts. Texts (here called documents) can be reviews about products or movies, articles, tweets, etc. In this article, we show you how to assign predefined sentiment labels to documents, using the KNIME Text Processing extension in combination with traditional KNIME learner and predictor nodes. A set of 2000 documents has been sampled from the training set of the Large Movie Review Dataset v1.0.
This is the fourth part of my deep learning series. In Artificial Neural Networks (ANNs), the output of one layer is used as an input to the next layer and so on. It is used to calculate the output of the network using basic linear algebra. Such neural networks are called Feed-Forward NNs. We will try to understand the algorithm using an example.
Today, I will walk you through the electrocardiogram (ECG) biomedical signal data with the aim of learning similarity representations between the two recorded signal data events. ECG is one of the most commonly heard types of signal data in context to human medical recordings. So, let's first simply understand what exactly is "Signal" in layman terms, what is an ECG signal, why is it needed, what exactly is Siamese Neural Network, how it can be useful towards comparing two vectors, and finally we will see an use-case starting with the ECG data analysis including uni/multivariate plotting, rolling window sum plots, data profiling, filtering outliers, detecting r-signal-to-signal peaks, and finally identifying the ECG signal similarities with Siamese Network model. The fundamental quantity of representing some information is called a "signal" in simple engineering terms. While in context to mathematical world, a signal is just a function that simply conveys some information, where the information could be a function of time [y y (t) ] or it could be function of spatial coordinates [ y y (x, y) ] or it could be a function of distance from source [ y y (r) ], etc. as an example.
In this part we are going to learn more about graphs concepts then we explain simple example about how to read karate club datasets: after this part we are ready to dig into graph convolution neural networks. The recent success of graph neural networks(GNNs) for analyzing the graphs' domain has attracted more researchers in this field. CNN is a type of deep learning model for processing data that has a sequence or grid pattern(text, images), which is inspired by the visual system of mammals organization and designed to automatically and adaptively multi-scale localized features, from low-to-high-level patterns. CNN is a mathematical framework typically composed of three types of layers ( convolution, pooling, and fully connected layers), and they apply for object detection, speech recognition, and other Euclidean data structures. Deep learning can extract meaningful local features of Euclidean data, such as images.
Recent advance in machine learning has made face recognition not a difficult problem. But in the previous, researchers have made various attempts and developed various skills to make computer capable of identifying people. One of the early attempt with moderate success is eigenface, which is based on linear algebra techniques. In this tutorial, we will see how we can build a primitive face recognition system with some simple linear algebra technique such as principal component analysis. Face Recognition using Principal Component Analysis Photo by Rach Teo, some rights reserved.
Word Vectorization is a methodology of mapping words from a vocabulary to to vectors of real numbers. These vectors can be used in various NLP ML models for performing various tasks like Text Similarity, Topic modeling, POS detection, prediction etc. Word Vectorization is a requirement for NLP ML models. NLP algorithms are used for extracting important information from text data. The Deep learning models works on numeric data. So we need to convert the text data to numeric form.
Vector space models are to consider the relationship between data that are represented by vectors. It is popular in information retrieval systems but also useful for other purposes. Generally, this allows us to compare the similarity of two vectors from a geometric perspective. In this tutorial, we will see what is a vector space model and what it can do. A Gentle Introduction to Vector Space Models Photo by liamfletch, some rights reserved.
As you are probably aware, graphs are extremely useful for encoding information, and data in graph format is increasingly plentiful. Many areas of machine learning, including natural language processing, computer vision, and recommendations, graphs are used to model local relationships between isolated data items (users, items, events, and others) and to construct global structures from local information. Representing data as graphs is often a necessary step (and at other times a desirable one) when dealing with problems arising from applications in machine learning or data mining. In particular, it becomes crucial when we want to apply graph-based learning methods to the datasets. The transformation from structured or unstructured data to a graph representation can be performed in a lossless manner, but this isn't always necessary (or desirable) for the purpose of the learning algorithm. Sometimes, a better model is an "aggregated view" of the data. For instance, if you're modeling a phone call between two people you can decide to have a relationship between the two entities (the caller and the receiver) for each call, or you can have a single relationship that aggregates all the calls.
Bob has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate the price of mobiles his company creates. In this competitive mobile phone market, you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies.
Topic modeling is a problem in natural language processing that has many real-world applications. Being able to discover topics within large sections of text helps us understand text data in greater detail. For many years, Latent Dirichlet Allocation (LDA) has been the most commonly used algorithm for topic modeling. The algorithm was first introduced in 2003 and treats topics as probability distributions for the occurrence of different words. If you want to see an example of LDA in action, you should check out my article below where I performed LDA on a fake news classification dataset.