Deep Learning with TensorFlow

Colleagues, Deep Learning professionals earn on average $137,989 per year according to ZipRecruiter. The Deep Learning with TensorFlow training program will equip you to build Deep Learning models in TensorFlow and Keras, interpret the results of Deep Learning models, troubleshoot and improve Deep Learning models, understand the language and fundamentals of artificial neural networks, and build your own Deep Learning projects. Skill-based training modules address: 1) Introduction to Deep Learning - artificial neural networks, and the breadth of transformative applications it produces. Jon also goes over how to run the code examples provided throughout the LiveLessons, and then he builds an introductory neural network with you, 2) How Deep Learning Works - main families of deep neural networks and their applications. The heart of the lesson is a high-level overview of the essential theory that underlies deep learning. To bring this theory to life, Jon shows you deep learning in action via a web application called the TensorFlow Playground, archetypal deep learning datasets, and then you build a deep neural network together to tackle a classic machine vision problem, 3) Convolutional Networks - convolutional layers and how they have can be stacked to solve increasingly complex problems with larger data sets. In order to make sense of the outputs from these sophisticated models, the TensorBoard result-visualization tool is added to your arsenal at the end of the lesson, 4) Introduction to TensFlow - high-level deep learning API Keras to build your models. In this lesson, the leading Deep Learning libraries are compared, and then you get down to business with TensorFlow, the open-source library doing the heavy neural network-lifting underneath Keras and, in Jon’s opinion, clearly the best choice from the options available. Given this, the second half of the lesson is dedicated to building your own deep learning models in TensorFlow, and 5) Improving Deep Networks - performance of your deep learning models, including by tuning model hyperparameters. The lesson concludes by discussing how to build your own deep learning project as well as outlining resources for further self-study.

Enroll today (teams & execs welcome): https://tinyurl.com/y6vek5nz 

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