1. Applications of Deep Learning and how to get started with implementation of deep learning
Presentation By : Manaswi Advisor : Dr.Chinmay Hegde

2. Application -1
Automatic Colorization of Black and White Images

3. Automatic Machine Translation
Application -2
Automatic Machine Translation

4. Automatic Image Caption Generation
Application -3
Automatic Image Caption Generation

5. All about building your first deep learning model
Tutorials:
- Brief than the book we are studying ( has implementation of deep learning on MNIST dataset with Theano)
- implementation of deep learning on MNIST dataset with Tensorflow
Packages
Install Anaconda – contains most of the scientific libraries used in machine learning Numpy, Scipy, matplotlib, scikitlearn (
Install Tensorflow – contains deep learning algorithms, different optimization techniques ( )
And that’s it you are ready to build your first deep learning model.

6. For those who want to get started with python
a decent resource to get started with python

7. Datasets MNIST – Large database of handwritten digit
Caltech Pictures of objects belonging to 101 categories
Image Net - 14,197,122 images, pictures of objects belonging to categories
MNIST
CALTECH-101
IMAGE NET

8. Forums
Kaggle community– hundreds of datasets to work with and 536,000 fellow members to help you. ( )
- datasets, research groups, jobs, tutorials everything in one place

9. Results on MNIST dataset:
K-NN:
Got accuracy up to 85%, but according to Yann Lecun if some tactics (like one vs all classification) are implemented then we can get accuracy up to 95%

10. Results on MNIST dataset:
Neural Networks
Quadratic cost Function:
1 hidden layer,30 neurons,30 epochs, mini batch size=10, learning rate=3 – %accuracy
1 hidden layer,100 neurons,30 epochs, mini batch size=10, learning rate=3 – %accuracy
Cross entropy cost function:
1 hidden layer,30 neurons,30 epochs, mini batch size=10, learning rate=0.5 – %accuracy
1 hidden layer,100 neurons,30 epochs, mini batch size=10, learning rate=0.5– %accuracy
1 hidden layer,100 neurons,60 epochs, mini batch size=10, learning rate=0.1, regularization parameter = 5.0 – 98.04%accuracy

11. Results on MNIST dataset:
Convolutional nets:
convolutional layer with 20 feature maps, 60 epochs, mini batch size=10, learning rate=0.1 – 99.06% accuracy
Bottom line convolutional nets perform better than neural nets and KNN when there is enough data

12. Features learnt by conv nets on MNIST dataset
Whiter blocks mean a smaller typically more negative weight, so the feature map responds less to corresponding input pixels. Darker blocks mean a larger weight, so the feature map responds more to the corresponding input pixels

13. GDXray Dataset

14. Accuracy on GDXray Dataset
Got accuracy of 96% with 2 Convolutional layers (each layer having 5x5 filters, 16 feature maps, 2x2 max pool) followed by fully connected layer (100 neurons) and a Softmax layer

15. Weights learnt by conv nets for GDXray dataset

16. The wonderful and terrifying implications of computers that can learn