Clarifai
Amazon Rekognition
Google Vision AI
OpenCV
Kairos
Keras
Trueface Visionbox
Microsoft Computer Vision API
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
Exploratory
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Clarifai is a leading deep learning AI platform for computer vision, natural language processing and automatic speech recognition. We help enterprises and public sector organizations transform unstructured images, video, text and audio data into structured data, significantly faster and more accurately than humans would be able to do on their own. Our technology is used across many industries including E-commerce, Defense, Retail, Manufacturing, and more.
Our platform is powered by state-of-the-art machine learning and comes with the broadest repository of pre-trained out-of-the-box AI models to search, sort, and organize visual, textual, and audio data and help companies build turnkey AI solutions. Our pre-trained models can detect explicit content, faces, embedded objects and text within images and video as well as predict various attributes such as celebrities, food items, textures, colors, and more. An intuitive, feature-rich user interface makes it easy to use for all skill levels. We offer a free API to researchers and developers to get started building their own models in the efforts of using AI to help the greater good.
Clarifai Scikit-learnNo features have been listed yet.
Based on our record, Scikit-learn seems to be more popular. It has been mentiond 27 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 11 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: 12 months ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: 12 months ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
Amazon Rekognition - Add Amazon's advanced image analysis to your applications.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Google Vision AI - Cloud Vision API provides a comprehensive set of capabilities including object detection, ocr, explicit content, face, logo, and landmark detection.
OpenCV - OpenCV is the world's biggest computer vision library
NumPy - NumPy is the fundamental package for scientific computing with Python
Kairos - Facial recognition & mood detection API