Visualize a variety of Neural Network architectures and even show how your pre-trained models process data in real-time
TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js, and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser.
By applying TensorSpace API, it is more intuitive for Data Scientists to visualize and understand any pre-trained models built by TensorFlow, Keras, TensorFlow.js, and so on.
In this quick and short course, you’ll learn how to present the inner workings of your pre-trained Neural Network models with easy-to-access 3D visualizations in a web browser.
By the end of this course, you’ll be able to create compelling 3D visualizations that will show the neural network architecture and how pre-trained models work in real time with TensorSpace
The code bundle for this video course is available at - https://github.com/PacktPublishing/3D-Neural-Network-Visualization-with-TensorSpace
About the Author
- Jakub Konczyk has enjoyed and done programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share it with others. He first discovered Machine Learning when he was trying to predict the real estate prices in one of the early stage start-ups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning that he would like to share with you in this course. It boils down to “Keep it simple!” mantra. Learn more at https://kubakonczyk.com
- Basic knowledge of Neural Network
- Prepare a pre-trained model for visualization from numerous neural network libraries like Keras, TensorFlow and Tensorflow.js
- Explore how each part of your neural network model is presented and learn how to change it
- Discover the inner-workings of your pre-trained model using a specific example in an easy and accessible way
- Develop new insights by looking at your neural network from a fresh perspective
- Discover how to easily present and explain your neural network architectures
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