*** NOW IN TENSORFLOW 2 and PYTHON 3 ***
Learn about one of the most powerful Deep Learning architectures yet!
The Convolutional Neural Network (CNN) has been used to obtain state–of–the–art results in computer vision tasks such as object detection, image segmentation, and generating photo–realistic images of people and things that don’t exist in the real world!
This course will teach you the fundamentals of convolution and why it’s useful for deep learning and even NLP (natural language processing).
You will learn about modern techniques such as data augmentation and batch normalization, and build modern architectures such as VGG yourself.
This course will teach you:
The basics of machine learning and neurons (just a review to get you warmed up!)
Neural networks for classification and regression (just a review to get you warmed up!)
How to model image data in code
How to model text data for NLP (including preprocessing steps for text)
How to build an CNN using Tensorflow 2
How to use batch normalization and dropout regularization in Tensorflow 2
How to do image classification in Tensorflow 2
How to do data preprocessing for your own custom image dataset
How to use Embeddings in Tensorflow 2 for NLP
How to build a Text Classification CNN for NLP (examples: spam detection, sentiment analysis, parts–of–speech tagging, named entity recognition)
Courses : 22
Specification: Deep Learning: Convolutional Neural Networks in Python
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