Deep Learning with TensorFlow (Application of Neural Networks)

Colleagues, the Deep Learning with TensorFlow - Applications of Deep Neural Networks to Machine Learning Tasks program brings machine-learning to life with interactive demos from the most popular Deep Learning library, TensorFlow, and its high-level API, Keras. Essential theory is whiteboarded to provide an intuitive understanding of Deep Learning’s underlying foundations, i.e., artificial neural networks plus provided Python-based Jupyter notebooks. This intermediate-level program includes over 6 hours of instruction. Learn 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 project. Skill-based training modules include: 1)  Introduction to Deep Learning - overview of deep learning, its roots in artificial neural networks, and the breadth of transformative applications it produces and builds an introductory neural network, 2) How Deep Learning Works - main families of deep neural networks and their applications, shows you deep learning in action via a web application called the TensorFlow Playground. He introduces the archetypal deep learning data sets, and then you build a deep neural network together to tackle a classic machine vision problem, 3) Convolutional Networks - construct a deep network. This lesson builds upon those theoretical foundations to build more effective deep nets, gain an understanding of convolutional layers and how they 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 used, 4) Introduction to TensorFlow - 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 build your own deep learning models in TensorFlow, and 5) Improving Deep Networks - tune model hyperparameters and create a deep learning project as well as outlining resources for further self-study.

Enroll today (individuals & teams welcome): https://tinyurl.com/uvyfnwwf 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy