Deep Learning: Convolutional Neural Networks in Python

Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow.

What Will I Learn?

• Understand convolution
• Understand how convolution can be applied to audio effects
• Understand how convolution can be applied to image effects
• Implement Gaussian blur and edge detection in code
• Implement a simple echo effect in code
• Understand how convolution helps image classification
• Understand and explain the architecture of a convolutional neural network (CNN)
• Implement a convolutional neural network in Theano
• Implement a convolutional neural network in TensorFlow

Requirements

• Install Python, Numpy, Scipy, Matplotlib, Scikit Learn, Theano, and TensorFlow
• Learn about backpropagation from Deep Learning in Python part 1
• Learn about Theano and TensorFlow implementations of Neural Networks from Deep Learning part 2

Who is the target audience?

• Students and professional computer scientists
• Software engineers
• Data scientists who work on computer vision tasks
• Those who want to apply deep learning to images
• Those who want to expand their knowledge of deep learning past vanilla deep networks
• People who don't know what backpropagation is or how it works should not take this course, but instead, take parts 1 and 2.
• People who are not comfortable with Theano and TensorFlow basics should take part 2 before taking this course.

Includes:

• 6 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

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Take this course