Learn to detect anomalies in large data sets to identify network intrusions using supervised and unsupervised machine learning techniques, such as accelerated XGBoost, autoencoders, and generative adversarial networks (GANs).
Discover how to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and use this information to map anomalies to failure conditions.
Discover how to quickly build and deploy production quality conversational AI applications with real-time transcription and natural language processing capabilities.
Explore the fundamental tools and techniques for building highly effective recommender systems, as well as how to deploy GPU-accelerated solutions for real-time recommendations.
Explore how to use Transformer-based natural language processing models for text classification tasks, such as categorizing documents. You will also explore how to leverage Transformer-based models for named-entity recognition (NER) tasks and analyze various model features, constraints, and characteristics to determine which model is best suited for a particular use case―based on metrics, domain specificity, and available resources.
Learn how deep learning (DL) works through hands-on exercises in computer vision and natural language processing (NLP). You will train deep learning models from scratch, and pick up tricks and tools for achieving highly accurate results along the way. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly
Find out how to use multiple GPUs to train neural networks and effectively parallelize\ntraining of deep neural networks using TensorFlow.
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