NVIDIA Deep Learning Institute
Part 1: An Introduction to Deep Learning
Part 2: How a Neural Network Trains
Part 3: Convolutional Neural Networks
Part 4: Data Augmentation and Deployment
Part 5: Pre-trained Models
Part 6: Advanced Architectures
Tools, libraries, and frameworks: Tensorflow, Keras, Pandas, NumPy
Learning Objectives
By participating in this workshop, you’ll:
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Learn the fundamental techniques and tools required to train a deep learning model
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Gain experience with common deep learning data types and model architectures
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Enhance datasets through data augmentation to improve model accuracy
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Leverage transfer learning between models to achieve efficient results with less data and computation
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Build confidence to take on your own project with a modern deep learning framework
1. The Mechanics of Deep Learning
Explore the fundamental mechanics and tools involved in successfully training deep neural networks:
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Train your first computer vision model to learn the process of training.
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Introduce convolutional neural networks to improve accuracy of predictions in vision applications.
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Apply data augmentation to enhance a dataset and improve model generalization.
2. Pre-trained Models and Recurrent Networks
Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data:
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Integrate a pre-trained image classification model to create an automatic doggy door.
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Leverage transfer learning to create a personalized doggy door that only lets in your dog.
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Train a model to autocomplete text based on New York Times headlines.
3. Final Project: Object Classification
Apply computer vision to create a model that distinguishes between fresh and rotten fruit.
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Create and train a model that interprets color images.
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Build a data generator to make the most out of small datasets.
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Improve training speed by combining transfer learning and feature extraction.