Clarifai
Amazon Rekognition
Google Vision AI
OpenCV
Kairos
Microsoft Computer Vision API
Keras
Trueface Visionbox
Scikit-learn
Pandas
OpenCV
NumPy
Dataiku
WEKA
htm.java
Exploratory
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-learnBased on our record, Scikit-learn seems to be more popular. It has been mentiond 31 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.
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
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 / almost 2 years 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