Deep Learning with TensorFlow Overview
This course teaches the core concepts of deep learning using TensorFlow,
Google’s open-source computation graph library.
Deep learning has become standard in the tech industry, achieving
state-of-the-art results in computer vision, natural language processing,
and artificial intelligence. TensorFlow provides the flexibility needed to
implement and research cutting edge architectures while allowing users to
focus on the structure of their models as opposed to mathematical minutiae.
Students will learn modern techniques with hands-on model building, data
collection/transformation, and deployment.
Deep learning is not an entry-level subject. In order to get the most out of the course, students should be familiar with the following:
- 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 and 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, and the GitHub platform are beneficial, but not required.
Considering the data science immersive bootcamp?
Part-Time Alumni can apply the amount of tuition paid for one part-time professional development course towards enrollment in an upcoming bootcamp upon admittance.
Upon completion of the Deep Learning with TensorFlow course, students have: