https://github.com/machine-learning-tokyo/dl-workshop-series

Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)
https://github.com/machine-learning-tokyo/dl-workshop-series

cnn-keras convolutional-neural-networks deep-learning keras workshop

Last synced: about 2 months ago
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Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT)

• Host: GitHub
• URL: https://github.com/machine-learning-tokyo/dl-workshop-series
• Owner: Machine-Learning-Tokyo
• License: apache-2.0
• Created: 2018-11-23T02:05:44.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-12-20T01:35:58.000Z (about 1 year ago)
• Last Synced: 2024-10-30T04:11:15.476Z (about 2 months ago)
• Topics: cnn-keras, convolutional-neural-networks, deep-learning, keras, workshop
• Language: Jupyter Notebook
• Size: 15 MB
• Stars: 938
• Watchers: 67
• Forks: 254
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DL-workshop-series

Code used for Deep Learning related workshops for **Machine Learning Tokyo (MLT)**

# Part I: Convolution Operations

## Implementation

[**ConvKernels**](https://github.com/Machine-Learning-Tokyo/DL-workshop-series/blob/master/Part%20I%20-%20Convolution%20Operations/ConvKernels.ipynb "ConvKernels"): colab notebook with simple examples of various kernels applied on an image using convolution operation

[**ConvNets**](https://github.com/Machine-Learning-Tokyo/DL-workshop-series/blob/master/Part%20I%20-%20Convolution%20Operations/ConvNets.ipynb "ConvNets"): colab notebook with functions for constructing keras models.

Models:

1. AlexNet

2. VGG

3. Inception

4. MobileNet

5. ShuffleNet

6. ResNet

7. DenseNet

8. Xception

9. Unet

10. SqueezeNet

11. YOLO

12. RefineNet

## Slides

Link to the presentation: https://drive.google.com/open?id=1sXztx3E9M3G0BIRLh6sxaqVOEOdJVJTrzHOixA5b-rM

Cheat Sheet: ![Alt text](https://github.com/Machine-Learning-Tokyo/DL-workshop-series/blob/master/Part%20I%20-%20Convolution%20Operations/ConvOpsCheatSheet.png?raw=true "Cheat Sheet: Conv. Operations")

## Video series: CNN Architectures (including implementation)

[![YouTube Playlist](https://github.com/Machine-Learning-Tokyo/DL-workshop-series/blob/master/Part%20I%20-%20Convolution%20Operations/CNN_architectures.png)](https://www.youtube.com/playlist?list=PLaPdEEY26UXywkvfCy0tmRoQorSSTfYq3)

# Part II: Learning in Deep Networks

[![YouTube Playlist](https://github.com/Machine-Learning-Tokyo/DL-workshop-series/blob/master/Part%20II%20-%20Learning%20in%20Deep%20Networks/DL_series.png)](https://www.youtube.com/playlist?list=PLaPdEEY26UXxvlzz485w61W4LgO0lUZfg "Lerning in Deep Networks Video Series")