Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support
Rich Ott (The Data Incubator)
BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.
Deep learning with TensorFlow
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of data flow graphs for numerical computations, with automatic parallelization across several CPUs or GPUs. This architecture makes it ideal for implementing neural networks and other machine learning algorithms. This training will introduce TensorFlow's capabilities through its Python interface.
Natural language processing with deep learning
Delip Rao (R7 Speech Science)
Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.
Create, Optimize, and Deploy a Deep Learning Solution using Tensorflow and Caffe
Ben Odom (Intel), Rudy Cazabon (Intel), Meghana Rao (Intel)
This hands-on training will leave attendees knowing how to build, implement and deploy a deep learning solution. Every registered attendee will receive free hardware to work with during the training. The hardware is yours to keep at the end of the event.
AI and Data Science for Managers
Michael Li (The Data Incubator), Zachary Glassman (The Data Incubator)
In this course, we will be offering a non-technical overview of AI and data science. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.
Neural networks for time series analysis using Deeplearning4j
Tom Hanlon (Functional Media)
Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Tom Hanlon demonstrates how to use Deeplearning4j to build recurrent neural networks for time series data.