Text Classification

How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. Why should you choose this course?

How this course will help you? A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. Why should you choose this course?

Giving and taking objects to and from humans are fundamental capabilities for collaborative robots in a variety of applications. NVIDIA researchers are hoping to improve these human-to-robot handovers by thinking about them as a hand grasp classification problem. In a paper called "Human Grasp Classification for Reactive Human-to-Robot Handovers", researchers at NVIDIA's Seattle AI Robotics Research Lab describe a proof of concept they claim results in more fluent human-to-robot handovers compared to previous approaches. The system classifies a human's grasp and plans a robot's trajectory to take the object from the human's hand. To do this, the researchers developed a perception system that can accurately identify a hand and objects in a variety of poses.

In this tutorial, I'll show you how to create a single label classification model in Google AutoML. We'll be using a dataset of AI-generated faces from generated.photos. We'll be training our algorithm to determine whether a face is male or female. After that, we'll deploy our model to the cloud AND create the web browser version of the algorithm. First let's take a look at the data we'll be classifying (you can download it here).

Learn to build cybertrolls detection engine with CNN, keras, Glove and popular programming language Python.NEW by Evergreen Technologies What you'll learn Detect cybertroll in social messages using CNN, Glove embeddings and Keras Description Course Description Learn to build cybertrolls detection engine with CNN, keras, Glove and popular programming language Python. Understanding of cybertrolls classification Understand the world of world embeddings Learn CNN from scratch Leverage CNN, Keras, Glove to classify cybertrolls in social messages Learn how to represent text as numeric vectors using glove embeddings Learn how to evaluate model from scratch User Jupyter Notebook for programming Build a real life web application to classify social messages A Powerful Skill at Your Fingertips Learning the fundamentals of text classification puts a powerful and very useful tool at your fingertips. Python and Jupyter are free, easy to learn, has excellent documentation. No prior knowledge of deep learning or Machine learning is assumed. I a, covering topics like CNN, Word Embeddings Precision, Recall in depth so that even beginners can understand this course very well.

Extreme multi-label text classification (XMTC) is an important problem in the era of {\it big data}, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of labels. We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels".

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself.

The second component is a suite of machine learning and natural language processing (NLP) models for segmenting potential customers. Web pages are fed into a multi-label convolutional neural network (CNN) text classification model that was developed by Yoon Kim. We further boost it by utilizing a pre-trained multi-lingual BERT language model developed by a team at Google to help scale across languages and classes with scarce training data. The data we use to train the model is enriched by crawling tens of thousands of company sites with labeled industry information found on Wikipedia. For companies without labels, we take advantage of the vast labeled Wikipedia corpus by employing semi-supervised learning.

Image classification tasks occupy the majority of machine learning experiments. Their critical usage in medical diagnosis, digital photography, self-driving cars and many others have attracted researchers to innovate models that would give near perfect prediction of the target object. Here, we have compiled a list of top-performing methods according to papers with code, on the widely popular datasets that are used for benchmarking the image classification models. ImageNet consists of more than 14 million images comprising classes such as animals, flowers, everyday objects, people and many more. Training a model on ImageNet gives it an ability to match the human-level vision, given the diversity of data.

Question Answering is a popular task in the field of Natural Language Processing and Information Retrieval, in which, the goal is to answer a natural language question (going beyond the document retrieval). Question or answer type classification plays a key role in question answering. The questions can be generally classified based on Wh-terms (Who, What, When, Where, Which, Whom, Whose, Why). Similarly, the answer type classification is determining the type of the expected answer based on the query. Such answer type classifications in literature are performed as a short-text classification task using a set of coarse-grained types, for instance, either 6 or 50 types with TREC QA task.