Introduction to Applied Deep Learning
This course provides an introduction to deep learning with a focus on practical applications, such as image and text analysis. The course will cover the intuition and theory behind what makes deep learning so successful in a succinct way. Participants will be introduced to deep learning and quickly gain experience applying it to real problems.
When checking out, select ticket type 3 courses: Python, Machine Learning, Deep Learning for the option to register for all three courses at the cost of only two.
If you would like to get more information on any of our classes or have questions, please email us at email@example.com.
- Convolutional Neural Networks
Practice: Convolutional neural networks for images, transfer learning
- Application of ConvNets
- Word embeddings
Practice: encode words into vector space, semantic document search
- Recurrent Neural Networks
Practice: RNNs for sentiment analysis
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About the Instructors:
Donald Miner is founder of the data science firm Miner & Kasch and specializes in large-scale data analysis enterprise architecture and applying machine learning to real-world problems. Donald is author of the O’Reilly book "MapReduce Design Patterns", and the O’Reilly reports “Hadoop with Python” and “What You Need To Know About Hadoop". He has architected and implemented dozens of mission-critical and large-scale data analysis systems within the U.S. Government and Fortune 500 companies. He has applied machine learning techniques to analyze data across several verticals, including financial, retail, telecommunications, healthcare, government intelligence, and entertainment. He lives in Maryland with his wife and three young sons.
Florian Muellerklein is a data scientist at Miner & Kasch and specializes in solutions which rely on deep learning. He has implemented artificial intelligence solutions for a variety of problems including domain specific language models and semantic document search, as well as state of the art computer vision solutions. Florian is a frequent competitor on Kaggle, a popular data science and machine learning competition website. As a master tier competitor he is ranked in the top 1% of all users sitewide. He has top finishes in competitions in: image classification, object localization, natural language processing, electroencephalogram recordings, and magnetic resonance imaging data.