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Do NOT train a ML model from scratch. I’ll show you some resources that have pre-trained models that you can use and modify. Check out the following: • Model Zoo curates pre-trained deep learning models for various frameworks and domains. You can find pre-trained models for computer vision, natural language processing, audio and speech, etc. • You can also find pre-trained ML models in the AWS marketplace. These can be quickly deployed on the Amazon SageMaker, which is a fully managed cloud mach
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Aick Amato on Instagram: "$50,000 dollar image classification 💰 Utilizing pre-trained models is a common and useful technique commonly known as transfer learning. This helps us avoiding recreating the wheel in terms of image classification. What we DO need to change, is the top layer of our pre-trained model. We need to specify what the model should be classifying images as and what it should be learning! Follow to see the full $50,000 code 🫡 #coding #datascience #python #deeplearning #ai #tec
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Here is how you can make this project 👇👇 In this project, I've utilized OpenCV for facial detection and Dlib for facial landmark detection. The green bounding box around the face is generated using the Haar Cascade Classifier from OpenCV, which is a pre-trained model for detecting objects in images. For emotion recognition, I used a Convolutional Neural Network (CNN) model trained on a dataset like FER-2013. This model processes the detected face and predicts the emotion by analyzing the facia
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