Artificial intelligence (AI) is being used to change our lives everyday. When it comes to building AI programs, there are two approaches programmers tend to choose: supervised or unsupervised machine learning. The simple distinction between these is supervised machine learning utilizes labeled data to predict outcomes, while unsupervised machine learning does not. There are, however, some differences between the two techniques, as well as critical areas where one surpasses the other. In this article, we will break down some of these differences with examples of both supervised and unsupervised learning.

Sentence Correction using RNN is simple problem in which we provide text data in corrupted form(gramatical mistake,short forms of some words like'ppl' for'people')to the input and output we get is the correct uncorrupted form of that text data.It can be used as a preprocessing step in a language transaltion model where the input language(in corrupted form) can be converted to uncorrupted form and then pass to a model to output the translated text and thus can help in increasing the efficiency of the language translation model.This case study will be useful for increasing the efficiency as many NLP tasks,since any model will learn from uncorrupted correct text and will be able to predict correctly the target task.Moreover it would be useful in text messaging apps where we could enter a corrupted text and it would suggest us the correct uncorrupted text before sending the text to anyone. Since the task at hand comprises of textual data,in which one form of corrupted textual data is to be converted into uncorrupted form while preserving the semantic meaning of the text.The task is similar to a language translation.The task can be converted to DL problem using LSTM's,GRU's and RNN.Since these archtectures help us to take in account the semantic meaning of text and we can use encoder decoder model to encode corrupted text and then decode it to uncorrupted form. We used to different datasets for our task.one Latency: As far as the latecy is concerned.Our model should output quickly(can take seconds) if it is used as preprocessing step for any other NLP tasks.But if it is used in a text messaging apps the output should be quick within milliseconds. As mention in the research paper, we will be using categorical cross entropy.

It could hardly be more complicated: tiny particles whir around wildly with extremely high energy, countless interactions occur in the tangled mess of quantum particles, and this results in a state of matter known as "quark-gluon plasma". Immediately after the Big Bang, the entire universe was in this state; today it is produced by high-energy atomic nucleus collisions, for example at CERN. Such processes can only be studied using high-performance computers and highly complex computer simulations whose results are difficult to evaluate. Therefore, using artificial intelligence or machine learning for this purpose seems like an obvious idea. Ordinary machine-learning algorithms, however, are not suitable for this task.

Artificial intelligence is being used for many extremely complex tasks. So why not use machine learning to study particle physics? As it turns out, this is not easy, because of some special mathematical properties of particle physics. But now, a neural network has been developed that can be used to study quark gluon plasma - the state of the universe after the Big Bang.

Move your ML skills from theory to practice in one of the most interesting fields " Physics"? In this course you are going to solve the glass identification problem where you are going to build and train several machine learning models in order to classify 7 types of glass( 1- Building windows float-processed glass / 2- Building windows non-float-processed glass / 3- Vehicle windows float-processed glass / 4- Vehicle windows non-float-processed-glass / 5- Containers glass / 6- Tableware glass / 7- Headlamps glass). After completing this course, you will gain a bunch of skillset that allows you to deal with any machine learning problem from the very first step to getting a fully trained performent model.

But first, let s address the question, What is computer vision? In simple terms, computer vision trains the computer to visualize the world just like we humans do. Computer vision techniques are developed to enable computers to see and draw analysis from digital images or streaming videos. The main goal of computer vision problems is to use the analysis from the digital source data to convert it into something about the world. Computer vision uses specialized methods and general recognition algorithms, making it the subfield of artificial intelligence and machine learning.

This article was published as a part of the Data Science Blogathon. In this article, we will be going through two concepts MLOps and DevOps. We will first try to get through their basics and then we will explore the differences between them. As you might be aware in DevOps we try to bring together the programming i.e development of web app or any software, it's testing mainly done by QA people and then its deployment. There is a whole machine learning model development life cycle that we try to streamline.

AI has the potential to generate meaningful returns for the enterprise. Responding to a 2018 PricewaterhouseCoopers survey, 54% of business executives say that their adoption of AI within the workplace has led to a boost in productivity. A separate 2019 McKinsey report found that 44% of firms using AI achieved a reduction in business costs in departments where AI is implemented. But barriers stand in the way of deployment, including a lack of production-grade data and expensive tools and development processes. Among the top challenges enterprises face in adopting AI is an absence of in-house talent.

Machine Learning Engineer at Seldon United Kingdom › London (Posted Jan 8 2022) Job description London OR Cambridge, UK (hybrid) Seldon is looking for talented Software Engineers with Machine Learning expertise to join our growing Engineering team. This role covers various positions in the software engineering team including backend product, open source MLOps and client facing machine learning engineers and can fit applicants from a range of seniority levels. We are focused on making it easy for machine learning models to be deployed and managed at scale in production. We provide Cloud Native products that run on top of Kubernetes and are open-core with several successful open source projects including Seldon Core, Alibi:Explain and Alibi:Detect. We also contribute to open source projects under the Kubeflow umbrella including KFServing.

When it comes to manufacturing new lightweight, yet strong components for new passenger jets, scientists are treating the process like trying to brew the most delicious cup of coffee. By using artificial intelligence (AI) and machine learning, researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are intelligently and automatically selecting the perfect settings for a different kind of hot brew -- the process of friction stir welding, a common ingredient needed to manufacture airplane components. In a new collaboration with GE Research, Edison Welding Institute and GKN Aerospace, Argonne computer scientists are putting the power of the laboratory's automated machine learning expertise and supercomputers to use. By reducing the number of costly experiments and time-consuming simulations with a new machine learning approach, they can generate accurate models that provide valuable information about the welding process in much less time and at a fraction of the cost. This approach, called DeepHyper, is a scalable automated machine learning package developed by Argonne computational scientist Prasanna Balaprakash and his colleagues at Argonne.