Packtpub – Learning Computer Vision with TensorFlow


Packtpub – Learning Computer Vision with TensorFlow
English | Size: 313.3 MB
Genre: eLearning


TensorFlow has been gaining immense popularity over the past few months, owing to its power and ease of use. This video aims to help you leverage the power of TensorFlow to perform image processing. Beginning with an introduction to image processing, the video will take you through TensorFlow’s API-like graph tensor, which can be used for image classification.

Starting off with basic 2D images, the video will gradually take you through recognizing more complex images, colors, shapes, and so on. Making use of the Python API, you will move on to classifying and training your model to identify more complex images such as face and expression detection, while you will also perform classification using regression.

Then you will delve into more advanced stuff such as semantic segmentation, Neural Image Caption Generation, and so on, taking advantage of TensorFlow’s Deep Neural Networks. Then the video will up the ante and cover advanced topics such as Object Tracking, Video stream processing, and, finally, accelerating image processing with a GPU.

Style and Approach

This video is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you can use TensorFlow and shows you how to use it in the context of real-world projects. This will not only give you the upper hand in the field but shows the potential for innovative uses of TensorFlow in your environment.

What You Will Learn

Build powerful multi-class image classifiers
Build a neural feature extractor that can embed images into a dense and rich vector space.
Perform fine-tuning optimization on new predictive tasks using pre-trained neural networks
Optimize a neural network with stochastic gradient descent and other advanced optimization methods
Build functional model classes and methods with TensorFlow-Keras’ Functional API
Choose the right loss function and evaluation metric for the right task
Build a computational graph representation of a Neural Network
Train a neural network with automatic back propagation


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