Created online video course about Machine and Deep Learning with Packt Publishing. Features full coverage of machine learning concepts from core fundamentals to working implementation. Early release is out now (here), final release out Q2 2018.
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field.
Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started.
I work here building the future of autonomous checkout on our deep learning team.
Deep Learning Consultant. Built deep learning systems for dynamic cyber threat and insurance modeling. Also worked on cybersecurity machine learning theory as it relates to the bounds on cyberinsurance market profitability.
Computer Vision Engineer. Developed automatic exercise classification from video with deep CNN’s. Developed facial recognition login, barbell path tracking, and form tracking systems.
Machine Learning Engineer. Worked on Recommendations team building machine learning models with big data pipelines. Ran user-facing experiments on Re-Ranking Homefeed using Facebook data signals. Built internal debug tool for our machine learning ranking models.
Software Engineer. Worked on iOS team. Leveraged runtime meta programming to implement property-based wrapper for NSUserDefaults. Worked on experimental Notifications Center Widget.