In this course, you will learn the fundamental principles of deep learning by building your very own deep learning library, similar to PyTorch, completely from scratch. This course includes all three courses in the Flamethrower Core series, for 33% off the price of buying all three separately. You'll start by learning how automatic differentiation works, and you'll build your own automatic differentiation engine based on the popular autograd library. From there, you'll be introduced to different neural network layer types, regularization strategies, loss functions, and more. As each of these concepts are introduced, you'll implement them in your library and test them out on real world data. We'll integrate material from statistics, numerical analysis, and Bayesian statistics to give you the most well rounded perspective of deep learning available in any course. By the time the course is finished, you'll have a solid grounding in both the theoretical and applied aspects of the field, and you'll have your own deep learning library to apply to problems of your own creation. View the entire course syllabus below, along with preview lessons. Be sure to click the drop down arrow to see the syllabus in its entirety.

Course Curriculum

  Course Introduction
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  Why Do We Care So Much About Neural Networks?
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  Automatic Differentiation
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  Neural Network Module
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  The Optimization Module
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  Pulling it all Together
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  Projects
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  Resources, References, and Course Credits
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Instructor: Daniel

Machine Learning Engineer and Researcher

I started Flamethrower AI to create a way to learn machine learning that actually works. I received my BA in applied mathematics with a focus on machine learning from UC Berkeley. I've worked in multiple industry and consulting roles, applying machine learning in areas as diverse as marketing and biotech.


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$199

Flamethrower Core - Unlimited Access