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) …
According to Gartner, hyperautomation is the number one trend in 2022 and will continue advancing in future. One of the main barriers to hyperautomation is in areas where we're still struggling to reduce human involvement. Intelligent systems have a hard time matching human visual recognition abilities, despite great advancements in deep learning in computer vision. This is mainly due to the lack of annotated data (or when data is sparse) and in areas such as quality control, where trained human eyes still dominate. Another reason is the feasibility of human access in all areas of the product supply chain, such as quality control inspection on the production line.
Not having sufficient data, time or resources represents a critical complication in building an efficient image classification network. In this article, I present a straightforward implementation where I get around all these lack-of-resource constraints. We will see what transfer learning is, why it is so effective, and finally, I will go step-by-step in building an image classification learning model. The model I will develop is an alpaca vs. not alpaca classifier, i.e. a neural network capable of recognizing whether or not the input image contains an alpaca. Finally, I will test the algorithm with some alpaca pictures I personally made during one of my recent hikes.
It is easy to be tricked by time-series models. I have seen models that are able to (seemingly) predict the most random trends accurately, such as stock and crypto prices, using advanced techniques that most don't fully understand. Is time series really like magic in this regard? Perform the right data manipulations, apply a complex-enough model, and presto, amazingly accurate predictions are produced for any date-indexed line into the future? If you have seen the same things I'm describing and are skeptical, you are right to feel that way.
Expand your NLP portfolio using BERT and Haystack to answer all your questions! If you're trying to learn Natural Language Processing (NLP), make a Discord Bot, or are just interested to play around with Transformers for a bit, this is the project for you! In this example, we will create a Chatbot that knows everything about Dragon Ball, but you can do about anything you want! It can be a chatbot that answers questions about another series, a university course, the laws of a country, etc. Firstly, let's see how that is possible with BERT. BERT is a Machine Learning technique for NLP created and published by Google in 2018.
One of the greatest promises of deep learning has been the advent of generated media. This is largely because generated media is one of the, currently, most easily monetized solutions that is offered by these frameworks. Generated media, regardless of format be it video, audio, text or others, has the potential to be translated into content for a plethora of different purposes. By harnessing this creative power, we can automate a huge portion of the creative process on associated tasks, and the technology has no reached the point that this content can even be sometimes indistinguishable from content made by true human actors. This is particularly true for NLP and computer vision related tasks.
Databricks is a business software startup that provides Data Engineering tools for processing and transforming massive amounts of data to develop machine learning models. Traditional Big Data procedures are not only slow to complete jobs but also take more time to build up Hadoop clusters. However, Databricks is built on top of distributed Cloud computing infrastructures like Azure, AWS, or Google Cloud, which allow programmes to execute on CPUs or GPUs according to analytical needs. In this article, we will be learning about building a machine learning model in Databricks. Following are the topics to be covered. In this article, we will be building a multivariate linear regression model for predicting the charges on insurance offered by the company based on different features.
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Having an environment capable of delivering Amazon SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams to launch AWS services. Organizations want to enable their developers to launch resources as they need them, but in a centralized and secure fashion. This post demonstrates how to centralize the management of SageMaker instance notebooks using AWS services including AWS CloudFormation, AWS Serverless Application Model (AWS SAM), AWS Service Catalog, Amazon EventBridge, AWS Systems Manager Parameter Store, Amazon API Gateway, and AWS Lambda.
This article was published as a part of the Data Science Blogathon. Amazon Sagemaker is arguably the most powerful, feature-rich, and fully managed machine learning service developed by Amazon. From creating your own labeled datasets to deploying and monitoring the models on production, Sagemaker is equipped to do everything. It can also provide an integrated Jupyter notebook instance for easy access to your data for exploration and analysis, so you don't have to fiddle around with server configuration. Sagemaker supports bring-your-own-algorithms and frameworks, which offer flexible distributed training options that adjust to your specific workflows.