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Udemy - Deep Learning with TensorFlow

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Description

 
Description  

Deep learning is the
intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the
newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making
sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the
possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and
simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.


With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data.
This course will offer you various complex algorithms for deep learning and various examples that use these deep neural
networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.
During the video course, you will come across topics such as logistic regression, convolutional neural networks,
recurrent neural networks, training deep networks, high level interfaces, and more. 

With the help of novel
practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond. 

About The Author 

Dan Van Boxel is a Data Scientist and Machine Learning Engineer with over 10 years of
experience. He is most well-known for “Dan Does Data,” a YouTube livestream demonstrating the power and pitfalls of
neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting
for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and
presented findings at the Transportation Research Board and other academic journals. 
Who this course is
for:  

 If you are a data scientist who performs machine learning on a regular basis, are familiar
with deep neural networks, and now want to gain expertise working with convoluted neural networks, then this course is
for you. 

Requirements  

 Some familiarity with C++ or Python is assumed. 

Last updated 8/2017 

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