Neural Networks and Deep Learning Course in Boston
December 17, 2018 @ 9:00 am - December 21, 2018 @ 6:00 pm
Are you a data scientists looking to gain experience in building and applying deep neural networks with most popular frameworks, such as Keras, TensorFlow, Theano, scikit-learn?
This 5-day Neural Networks and Deep Learning course in Boston is exactly what you need!
Join the training today to be able to enjoy the hands-on experience.
- Introduction to deep learning
- Neural networks
- Learning of neural networks
- Recurrent neural networks
- Generative adversarial networks
- Convolutional neural networks
- Deep learning in computer vision
- Image classification
- Object detection
- Deep learning in natural language processing
- Text classification
- Language modeling and sequence tagging
- Dialog systems
- Neural network ensemble
At the end of the course, all participants receive a certificate of completion. This certificate includes the training duration and contents, and proves the attendee’s knowledge of the emerging technology.
Vladimir Starostenkov has 10+ years of experience in software development. Over the course of his career, he has been part of 15 successful project implementations. Vladimir specializes in artificial intelligence and machine learning, distributed systems design, NoSQL and Hadoop-based systems benchmarking, permissioned blockchains, data engineering, and development of data-centric apps. As an expert in NoSQL databases, he has authored a number of research papers, comparing the performance of Apache Cassandra, Redis, MongoDB, and Couchbase.
Aleksandr Stefanin has a PhD in Economics and over 13 years of experience in R&D including more than 11 years of practice in data science and business analytics. He specializes in machine learning and artificial intelligence. Aleksandr has been featured in 50+ publications, including 20+ peer-reviewed articles.
Kanstantsin Buzanouski, Data Scientist
Kanstantsin Buzanouski is a Data Scientist who strives to solve business problems with a defined plan at all stages of the development process. He has considerable hands-on experience in using Machine Learning and statistical methods in various domains as well as solving business problems starting from problem definition up to model fine-tuning and solution deployment. He is profoundly knowledgeable about current trends and approaches to Machine
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