curiousily/Getting-Things-Done-with-Pytorch
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
This resource provides practical, step-by-step guides to apply advanced machine learning techniques to real-world business challenges. It takes raw data, such as images, text reviews, or time-series metrics, and transforms it into actionable insights like detected faces, predicted trends, or identified anomalies. Data scientists, machine learning engineers, and advanced data analysts who want to build and deploy deep learning solutions will find this useful.
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Use this if you are a data professional looking to implement and deploy deep learning models for tasks like image recognition, sentiment analysis, or forecasting.
Not ideal if you are a business user seeking a no-code solution or someone without a strong foundation in programming and machine learning concepts.
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