MrinmoiHossain/Udacity-Deep-Learning-Nanodegree

The course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in the course.

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Emerging

This resource provides comprehensive learning materials for anyone aspiring to become a deep learning engineer. It takes you from understanding basic neural networks to implementing advanced techniques like convolutional networks for image recognition and recurrent networks for sequence generation. The output is a strong foundation in deep learning theory and practical skills in PyTorch for building and deploying AI models.

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Use this if you are an aspiring AI engineer or data scientist looking to gain expertise in deep learning, particularly with the PyTorch framework.

Not ideal if you are a non-technical user looking for an out-of-the-box solution to a specific deep learning problem without learning the underlying principles.

deep-learning machine-learning-engineering neural-networks pytorch-development AI-model-deployment
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 21 / 25

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54

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33

Language

Jupyter Notebook

License

GPL-3.0

Last pushed

Aug 23, 2022

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