Image Recognition and Computer Vision
Image recognition enables computers to identify objects, people, places and actions in digital images. It is widely used in various fields, from social media to security systems. For instance, Facebook employs a sophisticated image recognition system called DeepFace to automatically tag friends in uploaded photos. The model analyzes facial features and matches them with existing profiles in its database, making the photo-sharing experience more interactive and engaging.
Behind the scene image annotation, segmentation and advanced techniques are used such as YOLO (You Only Look Once) algorithms and OpenCV.
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A good example is to match CVs with jobs. The system can automatically parse and extract key information from resumes, including skills, experience, and qualifications, while organizing them into predefined categories.
Using Natural Language Processing (NLP), it can understand the context and meaning of the text, distinguishing between similar terms in different contexts (like "Tesla" as a company versus a scientist).
When matching with job descriptions, the system uses similarity algorithms and classification models to calculate how well a candidate's profile matches the requirements.
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Python is one of the leading forces behind the development and growth of AI and ML in the software development realm. Our team uses Python and related tools, libraries, and frameworks to build state-of-the-art applications that incorporate artificial intelligence and machine learning solutions. We integrate AI and ML concepts into your applications by leveraging the power of tools like PyTorch, TensorFlow, or the Natural Language Toolkit.
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