What you'll learn :
Set up your computing environment and install TensorFlow
Apply logistic regression for classification with TensorFlow
Understand intuitively convolutional neural networks for image recognition
Learn to use TensorFlow with other types of networks
Build simple TensorFlow graphs for everyday computations
Design and train a multilayer neural network with TensorFlow
Bootstrap a neural network from simple to more accurate models
Program networks with SciKit-Flow, a high-level interface to TensorFlow
Some familiarity with C++ or Python is assumed.
With deep learning going mainstream for making sense of data, getting accurate results using deep networks is possible. This video is your guide to explore possibilities with deep learning. It will enable you to understand data like never before. With efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights which would change how you look at data.
With this video, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This video 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 like logistic regression, convolutional neural networks, training deep networks, and so on. With the help of practical examples, the video will cover advanced multilayer networks, image recognition, and beyond.
This course uses TensorFlow 0.8 and Python 3.5, while not the latest version available, it provides relevant and informative content for legacy users of TensorFlow, and Python.
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 aim to gain expertise in working with convoluted neural networks, then this course is for you.
Course Size Details :
2 hours on-demand video
2 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of completion
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