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) …
Pylearn2 is a machine learning library that has been designed to facilitate research projects. While it is admittedly not very easy to use and demands a good grasp of ML from the user, on the upside, it provides great flexibility to a researcher and is quite fast. About: While there are many resources to learn Pylearn2, this blog focuses on aspects of the library that are difficult to pick up on – getting your data in and making predictions out. To get data in, you need to write a Python wrapper class for your dataset, which it provides for, and which can further be used with multiclass sets. The blog has also provided for a hack in order to get predictions produced by Pylearn2.
There is a tremendous difference between data science for understanding and data science for prediction. The former is understanding why people use the emoji and what emotional states they are trying to communicate-- and how this differs across cultures and age groups. The latter is predicting that if someone types certain words in a certain order then the next emoji they'll type is . The former requires a rich and interdisciplinary set of skills -- mostly human skills -- as I first argued in a talk at Penn State in 2016. The latter is a purely technical problem -- and may even be a trivial technical problem -- and is just one part of the end-to-end data science process.
Many individuals picture a robot or a terminator when they catch wind of Machine Learning (ML) or Artificial Intelligence (AI). However, they aren't something out of motion pictures, it is anything but a cutting edge dream. We are living in a situation with numerous cutting edge applications developed using machine learning, despite that there are certain challenges an ML practitioner might face while developing an application from zero to bringing them to production. Data plays a key role in any use case. For beginners to experiment with machine learning, they can easily find data from Kaggle, UCI ML Repository etc.
This blog provides an overview of how to build a Machine Learning model with details on various aspects such as data pre-processing, splitting the training and testing data, regression/classification, and finally model evaluation. Machine Learning (ML) is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions. ML systems are trained rather than explicitly programmed. It provides efficient tools for data analysis, data pre-processing, model building, model evaluation, and much more. So in this blog we will implement various ML models with the help of Scikit learn(sk-learn), which is a simple open-source Machine Learning library.
Until recently, the use of artificial intelligence (AI) was only possible with great effort and construction of own neural networks. Today, the barrier to entering the world of AI through cloud computing services has fallen dramatically. Thus, one can immediately use current AI technology for the (partial) automation of the quality control of components without having to invest heavily in AI research. In this article, we show how such an AI system can be implemented exemplarily on the Google Cloud Platform (GCP). For this purpose, we train a model using AutoML and integrate it perspectively using Cloud Functions and App Engine into a process where manual corrections in quality control are possible.
I consult and educate companies to transform technology and data into a valuable, measurable, and monetizable business asset. In my data analytics and machine learning (ML) consulting engagements, I often come across use cases aimed at solving scientific problems using data, such as predicting the failure of a turbine or forecasting the carbon footprint of our IT data center. But what exactly is a scientific problem, and how is it different from a data problem? Is it really necessary to validate a known scientific fact or model again with data? Before answering these questions, let's define some key terms and scientific laws needed to answer these questions.
Have you ever trained a machine learning model that you've wanted to share with the world? Maybe set up a simple website where you (and your users) could try putting in their own inputs and seeing the models' predictions? It's easier than you might think! In this tutorial, I'm going to show you how to train a machine learning model to recognize digits using the Tensorflow library, and then create a web-based GUI to show predictions from that model. You (or your users) will be able to draw arbitrary digits into a browser, and see real-time predictions, just like below.
AI and advanced analytics can have a transformational impact on every aspect of a business, from the contact centre or supply chain to the overall business strategy. With the new challenges caused by coronavirus, companies are in a growing need of more advice, more data and visibility to minimise the business impact of the virus. However, long before the disruption caused by Covid-19, data was recognised as an essential asset in delivering improved customer service. And yet, businesses of all sizes have continued to struggle with gaining more tangible value from their vast hoards of data to improve the employee and customer experience. Data silos, creaking legacy systems and fast-paced, agile competitors have made the need to harness an organisations data to drive value of paramount importance.
In this Keras tutorial, you'll see how to Extend a Keras Model Generally, you only need your Keras model to return prediction values, but there are situations where you want your predictions to retain a portion of the input. A common example is forwarding unique'instance keys' while performing batch predictions. In this blog and corresponding notebook code, I'll demonstrate how to modify the signature of a trained Keras model to forward features to the output or pass through instance keys. Sometimes you'll have a unique instance key that is associated with each row and you want that key to be output along with the prediction so you know which row the prediction belongs to. You'll need to add keys when executing distributed batch predictions with a service like Cloud AI Platform batch prediction.
I was trying my hand on Optical Character Recognition on newspaper images when I realised that most documents have sections and text is not necessarily across the entire horizontal space of the page. Even though Tesseract was able to recognise the text it was jumbled up. To fix this the model should be able to identify sections on the document and draw a bounding box around it an perform OCR. It was this moment when applying Yolo Object detection on such images came into mind. YOLOv3 is extremely fast and accurate.