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

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  • Type Tutorials
  • Language English
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Infohash : 07B2EC43AA51EDC9FBA486366A0822CD8A40AC55



Udemy - Deep Learning Convolutional Neural Networks in Python

This is the 3rd part in my Data Science and Machine LearningĀ 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 forĀ computer vision usingĀ convolutional neural networks. These are the state of the art when it comes toĀ 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 theĀ StreetView House Number (SVHN)Ā dataset - which uses larger color images at various anglesĀ - 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

Files:

[GigaCourse.com] Udemy - Deep Learning Convolutional Neural Networks in Python 1. Outline and Review
  • 1. Introduction and Outline.mp4 (2.9 MB)
  • 1. Introduction and Outline.srt (3.5 KB)
  • 2. Review of Important Concepts.mp4 (5.7 MB)
  • 2. Review of Important Concepts.srt (6.4 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. How to Succeed in this Course.mp4 (6.4 MB)
  • 4. How to Succeed in this Course.srt (4.3 KB)
  • 5. Tensorflow or Theano - Your Choice!.mp4 (18.9 MB)
  • 5. Tensorflow or Theano - Your Choice!.srt (6.0 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)
2. Convolution
  • 1. Real-Life Examples of Convolution.mp4 (82.2 MB)
  • 1. Real-Life Examples of Convolution.srt (9.3 KB)
  • 2. Beginner's Guide to Convolution.mp4 (34.3 MB)
  • 2. Beginner's Guide to Convolution.srt (8.2 KB)
  • 3. What is convolution.mp4 (8.5 MB)
  • 3. What is convolution.srt (9.4 KB)
  • 4. Convolution example with audio Echo.mp4 (12.1 MB)
  • 4. Convolution example with audio Echo.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)
  • 6. Convolution example with images Edge Detection.mp4 (7.9 MB)
  • 6. Convolution example with images Edge Detection.srt (3.3 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)
3. Convolutional Neural Network Description
  • 1. Translational Invariance.mp4 (3.6 MB)
  • 1. Translational Invariance.srt (4.1 KB)
  • 2. Architecture of a CNN.mp4 (8.5 MB)
  • 2. Architecture of a CNN.srt (8.3 KB)
  • 3. Convolution on 3-D Images.mp4 (8.5 MB)
  • 3. Convolution on 3-D Images.srt (14.7 KB)
  • 4. Tracking Shapes in a CNN.mp4 (13.2 MB)
  • 4. Tracking Shapes in a CNN.srt (21.3 KB)
  • 5. Relationship to Biology.mp4 (3.9 MB)
  • 5. Relationship to Biology.srt (3.7 KB)
  • 6. Convolution and Pooling Gradients.mp4 (4.2 MB)
  • 6. Convolution and Pooling Gradients.srt (4.6 KB)
  • 7. LeNet - How the Shapes Go Together.mp4 (21.7 MB)
  • 7. LeNet - How the Shapes Go Together.srt (19.7 KB)
4. Convolutional Neural Network in Theano
  • 1. Theano - Building the CNN components.mp4 (7.0 MB)
  • 1. Theano - Building the CNN components.srt (7.2 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)
  • 3. Visualizing the Learned Filters.mp4 (8.9 MB)
  • 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)
  • 1. TensorFlow - Building the CNN components.srt (6.3 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)
6. Practical Tips
  • 1. Practical Image Processing Tips.mp4 (4.9 MB)
  • 1. Practical Image Processing Tips.srt (5.4 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)
7. Project Facial Expression Recognition
  • 1. Facial Expression Recognition Project Introduction.mp4 (9.8 MB)
  • 1. Facial Expression Recognition Project Introduction.srt (6.9 KB)
  • 2. Facial Expression Recognition Problem Description.mp4 (21.4 MB)
  • 2. Facial Expression Recognition Problem Description.srt (19.8 KB)
  • 3. The class imbalance problem.mp4 (10.1 MB)
  • 3. The class imbalance problem.srt (9.0 KB)
  • 4. Utilities walkthrough.mp4 (13.5 MB)
  • 4. Utilities walkthrough.srt (6.7 KB)
  • 5. Convolutional Net in Theano.mp4 (51.7 MB)
  • 5. Convolutional Net in Theano.srt (19.6 KB)
  • 6. Convolutional Net in TensorFlow.mp4 (47.7 MB)
  • 6. Convolutional Net in TensorFlow.srt (17.7 KB)
  • 7. Facial Expression Recognition Project Summary.mp4 (2.9 MB)
  • 7. Facial Expression Recognition Project Summary.srt (1.7 KB)
8. Appendix
  • 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. Windows-Focused Environment Setup 2018.mp4 (186.4 MB)
  • 2. Windows-Focused Environment Setup 2018.srt (21.6 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)
  • 4. How to Code by Yourself (part 1).mp4 (24.5 MB)
  • 4. How to Code by Yourself (part 1).srt (27.8 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)
  • 6. How to Uncompress a .tar.gz file.mp4 (5.4 MB)
  • 6. How to Uncompress a .tar.gz file.srt (4.4 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)
  • 8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (39.0 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 (0.9 KB)
  • [GigaCourse.com].url (0.0 KB)

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