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EVENT DETAILS |
Deep learning has become standard in the tech industry, achieving state-of-the-art results in computer vision, natural language processing, & artificial intelligence. This course teaches the core concepts of deep learning using TensorFlow, Google's open-source computation graph library.
Who the course is designed for:
Those who want to implement & research cutting-edge architectures while focusing on the structure of their models as opposed to mathematical minutiae. Those who want to learn modern techniques with hands-on model building, data collection/transformation, & deployment.
Outcomes
An understanding of the techniques, concepts, terminology, & mathematics of deep machine learning for computer vision & natural language processing.
Proficiency in TensorFlow to implement, train, visualize, export, & deploy deep models from scratch, as well as utilize pre-trained sources.
Skills for cleaning, normalizing, & generating data to make the most of available datasets.
Foundational knowledge of recent deep learning literature & experience implementing concepts introduced in those papers.
What you'll receive upon completion:
Certificate of completion
Certificate link & instructions on how to add to your LinkedIn profile
3.3 Continuing Education Units
Prerequisites
Deep learning is not an entry-level subject. In order to get the most out of the course, students should be familiar with basic statistics, basic linear algebra (matrix multiplication, transposing matrices), basic calculus (derivatives, summations), & programming (Python preferred, but those comfortable with another language should be able to learn the material).
There will be a pre-course workshop to refresh students on the requisite linear algebra & calculus techniques. Students with familiarity with NumPy will find it easier to pick up the material, but necessary components will be taught along with the rest of the course. Course materials will be hosted on GitHub, so knowledge of the bash console, Git, & the GitHub platform are beneficial but not required.
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