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Udemy - Deep Learning Convolutional Neural Networks in Python [GC]

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Udemy - Deep Learning Convolutional Neural Networks in Python [GC] (Size: 1 GB)
  1. Facial Expression Recognition Project Introduction.mp4 9.8 MB
  1. Facial Expression Recognition Project Introduction.srt 6.9 KB
  1. Introduction and Outline.mp4 2.9 MB
  1. Introduction and Outline.srt 3.5 KB
  1. Practical Image Processing Tips.mp4 4.9 MB
  1. Practical Image Processing Tips.srt 5.4 KB
  1. Real-Life Examples of Convolution.mp4 82.2 MB
  1. Real-Life Examples of Convolution.srt 9.3 KB
  1. TensorFlow - Building the CNN components.mp4 5.9 MB
  1. TensorFlow - Building the CNN components.srt 6.3 KB
  1. Theano - Building the CNN components.mp4 7 MB
  1. Theano - Building the CNN components.srt 7.2 KB
  1. Translational Invariance.mp4 3.6 MB
  1. Translational Invariance.srt 4.1 KB
  1. What is the Appendix.mp4 5.5 MB
  1. What is the Appendix.srt 3.8 KB
  10. Is Theano Dead.mp4 17.8 MB
  10. Is Theano Dead.srt 13.8 KB
  11. What order should I take your courses in (part 1).mp4 29.3 MB
  11. What order should I take your courses in (part 1).srt 17.1 KB
  12. What order should I take your courses in (part 2).mp4 37.6 MB
  12. What order should I take your courses in (part 2).srt 25.1 KB
  2. Advanced CNNs and how to Design your Own.mp4 19.6 MB
  2. Advanced CNNs and how to Design your Own.srt 15.8 KB
  2. Architecture of a CNN.mp4 8.5 MB
  2. Architecture of a CNN.srt 8.3 KB
  2. Beginner's Guide to Convolution.mp4 34.3 MB
  2. Beginner's Guide to Convolution.srt 8.2 KB
  2. Facial Expression Recognition Problem Description.mp4 21.4 MB
  2. Facial Expression Recognition Problem Description.srt 19.8 KB
  2. Review of Important Concepts.mp4 5.7 MB
  2. Review of Important Concepts.srt 6.4 KB
  2. TensorFlow - Full CNN and Test on SVHN.mp4 79.1 MB
  2. TensorFlow - Full CNN and Test on SVHN.srt 5.9 KB
  2. Theano - Full CNN and Test on SVHN.mp4 39.4 MB
  2. Theano - Full CNN and Test on SVHN.srt 6.9 KB
  2. Windows-Focused Environment Setup 2018.mp4 186.4 MB
  2. Windows-Focused Environment Setup 2018.srt 21.6 KB
  3. Convolution on 3-D Images.mp4 8.5 MB
  3. Convolution on 3-D Images.srt 14.7 KB
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 16.8 KB
  3. The class imbalance problem.mp4 10.1 MB
  3. The class imbalance problem.srt 9 KB
  3. Visualizing the Learned Filters.mp4 8.9 MB
  3. Visualizing the Learned Filters.srt 5.9 KB
  3. What is convolution.mp4 8.5 MB
  3. What is convolution.srt 9.4 KB
  3. Where to get the code and data for this course.mp4 5.6 MB
  3. Where to get the code and data for this course.srt 4.9 KB
  4. Convolution example with audio Echo.mp4 12.1 MB
  4. Convolution example with audio Echo.srt 6.7 KB
  4. How to Code by Yourself (part 1).mp4 24.5 MB
  4. How to Code by Yourself (part 1).srt 27.8 KB
  4. How to Succeed in this Course.mp4 6.4 MB
  4. How to Succeed in this Course.srt 4.3 KB
  4. Tracking Shapes in a CNN.mp4 13.2 MB
  4. Tracking Shapes in a CNN.srt 21.3 KB
  4. Utilities walkthrough.mp4 13.5 MB
  4. Utilities walkthrough.srt 6.7 KB
  5. Convolution example with images Gaussian Blur.mp4 12.3 MB
  5. Convolution example with images Gaussian Blur.srt 4.1 KB
  5. Convolutional Net in Theano.mp4 51.7 MB
  5. Convolutional Net in Theano.srt 19.6 KB
  5. How to Code by Yourself (part 2).mp4 14.8 MB
  5. How to Code by Yourself (part 2).srt 16.1 KB
  5. Relationship to Biology.mp4 3.9 MB
  5. Relationship to Biology.srt 3.7 KB
  5. Tensorflow or Theano - Your Choice!.mp4 18.9 MB
  5. Tensorflow or Theano - Your Choice!.srt 6 KB
  6. Convolution and Pooling Gradients.mp4 4.2 MB
  6. Convolution and Pooling Gradients.srt 4.6 KB
  6. Convolution example with images Edge Detection.mp4 7.9 MB
  6. Convolution example with images Edge Detection.srt 3.3 KB
  6. Convolutional Net in TensorFlow.mp4 47.7 MB
  6. Convolutional Net in TensorFlow.srt 17.7 KB
  6. How to Uncompress a .tar.gz file.mp4 5.4 MB
  6. How to Uncompress a .tar.gz file.srt 4.4 KB
  6. How to load the SVHN data and benchmark a vanilla deep network.mp4 10.1 MB
  6. How to load the SVHN data and benchmark a vanilla deep network.srt 4.4 KB
  7. Facial Expression Recognition Project Summary.mp4 2.9 MB
  7. Facial Expression Recognition Project Summary.srt 1.7 KB
  7. How to Succeed in this Course (Long Version).mp4 18.3 MB
  7. How to Succeed in this Course (Long Version).srt 15.5 KB
  7. LeNet - How the Shapes Go Together.mp4 21.7 MB
  7. LeNet - How the Shapes Go Together.srt 19.7 KB
  7. Write Convolution Yourself.mp4 18.3 MB
  7. Write Convolution Yourself.srt 11.6 KB
  8. Alternative Views on Convolution.mp4 10.2 MB
  8. Alternative Views on Convolution.srt 8.7 KB
  8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39 MB
  8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 33.9 KB
  9. Python 2 vs Python 3.mp4 7.8 MB
  9. Python 2 vs Python 3.srt 6.7 KB
  Readme.txt 921.6 B
  [GigaCourse.com].url 0 B
  ▲ 96 total files

Description


Udemy - Deep Learning Convolutional Neural Networks in Python

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!

For more Udemy Courses: https://gigacourse.com

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