{"id":9497,"date":"2026-08-10T10:08:07","date_gmt":"2026-08-10T10:08:07","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/linkedin-full-stack-deep-learning-with-python-2024-2\/"},"modified":"2026-08-10T10:08:07","modified_gmt":"2026-08-10T10:08:07","slug":"linkedin-full-stack-deep-learning-with-python-2024-2","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/linkedin-full-stack-deep-learning-with-python-2024-2\/","title":{"rendered":"LinkedIn \u2013 Full-Stack Deep Learning with Python 2024-2"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Full-Stack Deep Learning with Python Course. This comprehensive course provides hands-on training in full-stack deep learning concepts with Python, with an emphasis on MLOps and MLflow. Taught by Janani Ravi, a Google Certified Cloud Architect and Data Engineer, the course begins with an overview of the basic concepts of full-stack deep learning, MLOps, and MLflow. Participants then set up their workspace in Google Colab and work with MLflow. They then learn how to load and examine datasets, record metrics, parameters, and artifacts. The main parts of the course include model training, performance evaluation, hyperparameter tuning, model deployment, and making predictions. Throughout the course, participants work with the EMNIST dataset, configuring and training DNN and CNN models for image classification. Hyperparameter optimization techniques with Hyperopt and MLflow, identifying the best model and registering it in the MLflow registry are also covered. Finally, methods for deploying the model on a local machine and serving it are taught. This course is suitable for those who want to gain a deeper understanding of full-stack deep learning with Python and the full cycle of machine learning model development.<\/span><\/p>\n

What you will learn:<\/span><\/h3>\n