Map Generation using Deep Learning

1.Concepts learnt:

Python programming language from the book "Automate the boring stuff with python"
Machine Learning course from Stanford university through Coursera(by Andrew NG)

2.Implemented Linear Regression algorithm to find the line of best fit for a given sample of points.

3.Trained a model to classify handwritten digits whose images are 8 X 8 pixels.

4.Trained a model to classify handwritten digits whose images are 28 X 28 pixels.

5.Implemented GAN on MNIST handwritten digits dataset.

Name		Name	Last commit message	Last commit date
Latest commit History 21 Commits
CNN implementation for CIFAR10 dataset.ipynb		CNN implementation for CIFAR10 dataset.ipynb
DC GAN.ipynb		DC GAN.ipynb
GAN_mnist_working.ipynb		GAN_mnist_working.ipynb
Handwritten digits 28X28.ipynb		Handwritten digits 28X28.ipynb
Handwritten_Digits_8X8.ipynb		Handwritten_Digits_8X8.ipynb
Linear_Regression.ipynb		Linear_Regression.ipynb
README.md		README.md
logistic regression.ipynb		logistic regression.ipynb
multivariable regression.ipynb		multivariable regression.ipynb

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