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In this course we continue our journey deeper into machine learning, with a look at popular deep learning techniques. This course focuses heavily on neural networks and the various architectures used to solve problems no other machine learning techniques can solve. From multi-layer perceptrons to convolutional neural networks and recurrent neural networks, we explore the pros and cons of each technique. Each topic provides a theoretical understanding paired with a hands-on exercise to be solved in python and real word data.
This course is for anyone with a basic understanding of machine learning, linear algebra, and Python. The knowledge gained in this course can be used to open the gateway to deep learning research. This class certification will be advantageous to anyone seeking a deep learning role in industry or looking to pursue an advanced degree in artificial intelligence.
Dr. Sterling Ramroach
Sterling has a PhD in Machine Learning from The University of the West Indies. He moved to London after his PhD to pursue a Computer Vision Researcher position in industry. His PhD research focused on accelerating machine learning techniques with parallel processing and applying it to bioinformatics. His work is published in peer-reviewed scientific journals including Molecular Omics, Expert Systems with Application, and BMJ Innovations.
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