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[GigaCourse.com] Udemy - Deep Learning Convolutional Neural Networks in Python

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Description
Udemy - Deep Learning Convolutional Neural Networks in Python



Description

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU.

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.

Deep Learning: Convolutional Neural Networks in Python 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.

Suggested Prerequisites:

matrix addition and multiplication
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
Know the basic theory behind feedforward neural networks
Can write a feedforward neural network in Theano or TensorFlow

TIPS (for getting through the course):

Watch it at 2x.
Take handwritten notes. This will drastically increase your ability to retain the information.
Write down the equations. If you don't, I guarantee it will just look like gibberish.
Ask lots of questions on the discussion board. The more the better!
Realize that most exercises will take you days or weeks to complete.
Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Created by Lazy Programmer Inc.
Last updated 2/2020
English
English [Auto-generated]

File list
  • [GigaCourse.com] Udemy - Deep Learning Convolutional Neural Networks in Python
  • 1. Outline and Review/1. Introduction and Outline.mp4 2.9 MB
  • 1. Outline and Review/1. Introduction and Outline.srt 3.5 KB
  • 1. Outline and Review/2. Review of Important Concepts.mp4 5.7 MB
  • 1. Outline and Review/2. Review of Important Concepts.srt 6.4 KB
  • 1. Outline and Review/3. Where to get the code and data for this course.mp4 5.6 MB
  • 1. Outline and Review/3. Where to get the code and data for this course.srt 4.9 KB
  • 1. Outline and Review/4. How to Succeed in this Course.mp4 6.4 MB
  • 1. Outline and Review/4. How to Succeed in this Course.srt 4.3 KB
  • 1. Outline and Review/5. Tensorflow or Theano - Your Choice!.mp4 18.9 MB
  • 1. Outline and Review/5. Tensorflow or Theano - Your Choice!.srt 6 KB
  • 1. Outline and Review/6. How to load the SVHN data and benchmark a vanilla deep network.mp4 10.1 MB
  • 1. Outline and Review/6. How to load the SVHN data and benchmark a vanilla deep network.srt 4.4 KB
  • 2. Convolution/1. Real-Life Examples of Convolution.mp4 82.2 MB
  • 2. Convolution/1. Real-Life Examples of Convolution.srt 9.3 KB
  • 2. Convolution/2. Beginner's Guide to Convolution.mp4 34.3 MB
  • 2. Convolution/2. Beginner's Guide to Convolution.srt 8.2 KB
  • 2. Convolution/3. What is convolution.mp4 8.5 MB
  • 2. Convolution/3. What is convolution.srt 9.4 KB
  • 2. Convolution/4. Convolution example with audio Echo.mp4 12.1 MB
  • 2. Convolution/4. Convolution example with audio Echo.srt 6.7 KB
  • 2. Convolution/5. Convolution example with images Gaussian Blur.mp4 12.3 MB
  • 2. Convolution/5. Convolution example with images Gaussian Blur.srt 4.1 KB
  • 2. Convolution/6. Convolution example with images Edge Detection.mp4 7.9 MB
  • 2. Convolution/6. Convolution example with images Edge Detection.srt 3.3 KB
  • 2. Convolution/7. Write Convolution Yourself.mp4 18.3 MB
  • 2. Convolution/7. Write Convolution Yourself.srt 11.6 KB
  • 2. Convolution/8. Alternative Views on Convolution.mp4 10.2 MB
  • 2. Convolution/8. Alternative Views on Convolution.srt 8.7 KB
  • 3. Convolutional Neural Network Description/1. Translational Invariance.mp4 3.6 MB
  • 3. Convolutional Neural Network Description/1. Translational Invariance.srt 4.1 KB
  • 3. Convolutional Neural Network Description/2. Architecture of a CNN.mp4 8.5 MB
  • 3. Convolutional Neural Network Description/2. Architecture of a CNN.srt 8.3 KB
  • 3. Convolutional Neural Network Description/3. Convolution on 3-D Images.mp4 8.5 MB
  • 3. Convolutional Neural Network Description/3. Convolution on 3-D Images.srt 14.7 KB
  • 3. Convolutional Neural Network Description/4. Tracking Shapes in a CNN.mp4 13.2 MB
  • 3. Convolutional Neural Network Description/4. Tracking Shapes in a CNN.srt 21.3 KB
  • 3. Convolutional Neural Network Description/5. Relationship to Biology.mp4 3.9 MB
  • 3. Convolutional Neural Network Description/5. Relationship to Biology.srt 3.7 KB
  • 3. Convolutional Neural Network Description/6. Convolution and Pooling Gradients.mp4 4.2 MB
  • 3. Convolutional Neural Network Description/6. Convolution and Pooling Gradients.srt 4.6 KB
  • 3. Convolutional Neural Network Description/7. LeNet - How the Shapes Go Together.mp4 21.7 MB
  • 3. Convolutional Neural Network Description/7. LeNet - How the Shapes Go Together.srt 19.7 KB
  • 4. Convolutional Neural Network in Theano/1. Theano - Building the CNN components.mp4 7 MB
  • 4. Convolutional Neural Network in Theano/1. Theano - Building the CNN components.srt 7.2 KB
  • 4. Convolutional Neural Network in Theano/2. Theano - Full CNN and Test on SVHN.mp4 39.4 MB
  • 4. Convolutional Neural Network in Theano/2. Theano - Full CNN and Test on SVHN.srt 6.9 KB
  • 4. Convolutional Neural Network in Theano/3. Visualizing the Learned Filters.mp4 8.9 MB
  • 4. Convolutional Neural Network in Theano/3. Visualizing the Learned Filters.srt 5.9 KB
  • 5. Convolutional Neural Network in TensorFlow/1. TensorFlow - Building the CNN components.mp4 5.9 MB
  • 5. Convolutional Neural Network in TensorFlow/1. TensorFlow - Building the CNN components.srt 6.3 KB
  • 5. Convolutional Neural Network in TensorFlow/2. TensorFlow - Full CNN and Test on SVHN.mp4 79.1 MB
  • 5. Convolutional Neural Network in TensorFlow/2. TensorFlow - Full CNN and Test on SVHN.srt 5.9 KB
  • 6. Practical Tips/1. Practical Image Processing Tips.mp4 4.9 MB
  • 6. Practical Tips/1. Practical Image Processing Tips.srt 5.4 KB
  • 6. Practical Tips/2. Advanced CNNs and how to Design your Own.mp4 19.6 MB
  • 6. Practical Tips/2. Advanced CNNs and how to Design your Own.srt 15.8 KB
  • 7. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp4 9.8 MB
  • 7. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.srt 6.9 KB
  • 7. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 21.4 MB
  • 7. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.srt 19.8 KB
  • 7. Project Facial Expression Recognition/3. The class imbalance problem.mp4 10.1 MB
  • 7. Project Facial Expression Recognition/3. The class imbalance problem.srt 9 KB
  • 7. Project Facial Expression Recognition/4. Utilities walkthrough.mp4 13.5 MB
  • 7. Project Facial Expression Recognition/4. Utilities walkthrough.srt 6.7 KB
  • 7. Project Facial Expression Recognition/5. Convolutional Net in Theano.mp4 51.7 MB
  • 7. Project Facial Expression Recognition/5. Convolutional Net in Theano.srt 19.6 KB
  • 7. Project Facial Expression Recognition/6. Convolutional Net in TensorFlow.mp4 47.7 MB
  • 7. Project Facial Expression Recognition/6. Convolutional Net in TensorFlow.srt 17.7 KB
  • 7. Project Facial Expression Recognition/7. Facial Expression Recognition Project Summary.mp4 2.9 MB
  • 7. Project Facial Expression Recognition/7. Facial Expression Recognition Project Summary.srt 1.7 KB
  • 8. Appendix/1. What is the Appendix.mp4 5.5 MB
  • 8. Appendix/1. What is the Appendix.srt 3.8 KB
  • 8. Appendix/10. Is Theano Dead.mp4 17.8 MB
  • 8. Appendix/10. Is Theano Dead.srt 13.8 KB
  • 8. Appendix/11. What order should I take your courses in (part 1).mp4 29.3 MB
  • 8. Appendix/11. What order should I take your courses in (part 1).srt 17.1 KB
  • 8. Appendix/12. What order should I take your courses in (part 2).mp4 37.6 MB
  • 8. Appendix/12. What order should I take your courses in (part 2).srt 25.1 KB
  • 8. Appendix/2. Windows-Focused Environment Setup 2018.mp4 186.4 MB
  • 8. Appendix/2. Windows-Focused Environment Setup 2018.srt 21.6 KB
  • 8. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
  • 8. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 16.8 KB
  • 8. Appendix/4. How to Code by Yourself (part 1).mp4 24.5 MB
  • 8. Appendix/4. How to Code by Yourself (part 1).srt 27.8 KB
  • 8. Appendix/5. How to Code by Yourself (part 2).mp4 14.8 MB
  • 8. Appendix/5. How to Code by Yourself (part 2).srt 16.1 KB
  • 8. Appendix/6. How to Uncompress a .tar.gz file.mp4 5.4 MB
  • 8. Appendix/6. How to Uncompress a .tar.gz file.srt 4.4 KB
  • 8. Appendix/7. How to Succeed in this Course (Long Version).mp4 18.3 MB
  • 8. Appendix/7. How to Succeed in this Course (Long Version).srt 15.5 KB
  • 8. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39 MB
  • 8. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33.9 KB
  • 8. Appendix/9. Python 2 vs Python 3.mp4 7.8 MB
  • 8. Appendix/9. Python 2 vs Python 3.srt 6.7 KB
  • Readme.txt 962 B

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