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How To Build and Deploy an NLP Model with FastAPI: Part 1

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Model deployment is one of the most important skills you should have if you're going to work with NLP models. Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output for decision-making for a specific use case. There are different ways you can deploy your NLP model into production, you can use Flask, Django, Bottle e.t.c .But in today's article, you will learn how to build and deploy your NLP model with FastAPI. In part 1, we will focus on building an NLP model that can classify movie reviews into different sentiments.


Offline Data Augmentation for multiple images in Python

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Can you increase the number of images in any dataset? Machine learning, Deep learning, Artificial intelligence all require large amounts of data. However, data is not always available in every case. The programmer needs to work with the small amount of data available. Hence the use of data augmentation came into the picture.


Engineers Apply Physics-informed Machine Learning To Solar Cell Production - AI Summary

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Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.


Python Programming: Machine Learning, Deep Learning

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Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work. Welcome to the "Python Programming: Machine Learning, Deep Learning Python" course. In this course, we will learn what is Deep Learning and how does it work.


Dashboards for Interpreting & Comparing Machine Learning Models

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With the advent of technology, there are multiple machine learning algorithms in the Data Science field which makes it really difficult for a User/Data Scientist/ML Engineer to select the best model according to the dataset that they are working on. Comparing different models can be one way of selecting the best model, but it is time taking process where we will create different machine learning models and then compare their performance. It is also not feasible because most of the models are black-box and we don't know what is going on inside the model and how it will behave. In short, we don't know how to interpret the model because of the model complexity and model being black-box. Without interpreting the models it is difficult to understand how a model is behaving and how it will behave on the new data provided to it.


A Gentle Introduction to Multiple-Model Machine Learning

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An ensemble learning method involves combining the predictions from multiple contributing models. Nevertheless, not all techniques that make use of multiple machine learning models are ensemble learning algorithms. It is common to divide a prediction problem into subproblems. For example, some problems naturally subdivide into independent but related subproblems and a machine learning model can be prepared for each. It is less clear whether these represent examples of ensemble learning, although we might distinguish these methods from ensembles given the inability for a contributing ensemble member to produce a solution (however weakly) to the overall prediction problem.


Deployment of Machine Learning Models in Production

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Are you ready to kickstart your Advanced NLP course? Are you ready to deploy your machine learning models in production at AWS? You will learn each and every steps on how to build and deploy your ML model on a robust and secure server at AWS. Prior knowledge of python and Data Science is assumed. If you are AN absolute beginner in Data Science, please do not take this course. This course is made for medium or advanced level of Data Scientist.


Microsoft Azure AI Fundamentals

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In this article, we'll talk about Microsoft AI, the pathway to learn for beginners who are curious to explore the Microsoft AI Platforms, various functionalities and features supported by Machine Learning Studio in Azure, and the processes to train and better the Machine Learning Models with Azure. We also learn about different algorithms and thus gain the overall knowledge to get started and work with Microsoft Azure AI. Check out the official website of the summit to register as an attendee or to be a speaker and share your knowledge with the community. Microsoft AI is a powerful framework that enables organizations, researchers, and non-profits to use AI technologies with its powerful framework which offers services and features across domains of Machine Learning, Robotics, Data Science, IoT, and many more. One of the advantages of Azure can be realized with this example of how Machine Learning becomes more scalable in the Cloud even while working on Notebooks.


Explaining Bias In Your Data

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Over the last five years, unfairness in machine learning has gone from almost unknown to hitting the headlines frequently, and new cases of unwanted bias introduced in automated processes are frequently discovered. However, there is still no "one-size-fits-all" standard machine learning tool to prevent and assess such bias. In this article, we will deal with how to explain unfairness in a machine learning algorithm. In 2014, in a report called Big Data: Seizing Opportunities and Preserving Values, the Executive Office of President Obama pointed out the fact that "big data technologies can cause societal harms beyond damages to privacy, such as discrimination against individuals and groups". This was the first time unfairness in machine learning had been officially recognized as a potential harm.


Machine Learning Models

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In practice, "applying machine learning" means that you apply an algorithm to data, and that algorithm creates a model that captures the trends in the data. There are many different types of machine learning models to choose from, and each has its own characteristics that may make it more or less appropriate for a given dataset. This page gives an overview of different types of machine learning models available for supervised learning; that is, for problems where we build a model to predict a response. Within supervised learning there are two categories of models: regression (when the response is continuous) and classification (when the response belongs to a set of classes).