UDEMY 2021 - Deep Learning: Convolutional Neural Networks in Python
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Computer Vision and Data Science and Machine Learning combined! In Theano and TensorFlow.
What Will I Learn?
Understand how convolution can be applied to audio effects
Understand how convolution can be applied to image effects
This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.
In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset – which uses larger color images at various angles – so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge!
Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I’m going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I’m going to show you how to build filters for image effects, like the Gaussian blur and edge detection.
We will also do some biology and talk about how convolutional neural networks have been inspired by the animal visual cortex.
After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time (in part 2) with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.
All the materials for this course are FREE. You can download and install Python, Numpy, Scipy, Theano, and TensorFlow with simple commands shown in previous courses.
This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples
In the directory: cnn_class
Make sure you always “git pull” so you have the latest version!
HARD PREREQUISITES / KNOWLEDGE YOU ARE ASSUMED TO HAVE:
TIPS (for getting through the course):
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Who is the target audience?
Created by Lazy Programmer Inc.Last updated 8/2018EnglishEnglish [Auto-generated]
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