This project of Self driving car has been broken down into sections where in each section I have got to learn many things.

In glimpse -

• Used Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.

• Learned to train a Perceptron-based Neural Network to classify between binary classes.

• Learned to train Convolutional Neural Networks to identify between various traffic signs.

• Trained Deep Neural Networks to fit complex datasets.

• Mastered Keras, a power Neural Network library written in Python.

Builded and trained a fully functional self driving car to drive on its own!

Started with these topics ,

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Computer vision -

• Detecting lanes using OpenCV
• covering algorithms like, Canny algorithm , Hought transform
• link - https://github.com/Sahana-M/self-driving-car/tree/master/finding-lanes

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Perceptron -

• Learnt how a perceptron works and how to build a model using Keras
• Which factors decide the efficiency of a model
• learnt sigmoid activation function , Adam optimizer, Binary cross entropy for binary classification
• Solved a simple prediciting a diabetic patient or not use case using this model
• Used only a single hidden layer
• link - https://github.com/Sahana-M/Keras-implementations-DNN-CNN/blob/master/perceptron23.ipynb

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Keras classification -

• This time working on a difficult data set which is a non linear data set
• Used Keras to classify this dataset
• This time with more layers of DNN , 2 Hidden layers
• link - https://github.com/Sahana-M/Keras-implementations-DNN-CNN/blob/master/deep_neural_network.ipynb

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Multiclass classification using Keras -

• Implemented multiclass classification using Keras
• This time used One hot encoding, softmax activation function , and categorical cross entropy as it is multiclass classification
• link - https://github.com/Sahana-M/Keras-implementations-DNN-CNN/blob/master/multiclass_classification.ipynb

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DNN MNIST Image Recognition -

• Used Deep Neural networks on MNIST data set to recognise Handwritten digits using Keras
• Used relu and softmax activation functions
• Model obtained an accuracy of ~93
• link - https://github.com/Sahana-M/Keras-implementations-DNN-CNN/blob/master/MNIST%20Deep%20learning.ipynb

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CNN MNIST Image Recognition -

• Used Convolutional neural networks this time on MNIST data set to recognise Handwritten digits
• This time did a bit of image preprocessing, One hot encoding
• Used LenNet based model to classify our MNIST data set
• accuracy ~99%, better than DNN
• link - https://github.com/Sahana-M/Handwriting-recognition-using-Convolutional-neural-network-using-Keras

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Classifying Road signs -

• dataset - https://bitbucket.org/jadslim/german-traffic-signs
• Used modified LeNet based model
• Used various measures to improve the accuracy -
  • fine tuning
  • dropout layers
  • fit generator
  • data augmentation
• link - https://github.com/Sahana-M/self-driving-car/tree/master/identifying%20traffic%20signs

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Polynomial Regression -

• A simple implementation of polynomial regression using Keras which is very much necessary for Self driving car concepts
• link - https://github.com/Sahana-M/self-driving-car/blob/master/Polynomial_regression.ipynb

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Behavioural Cloning -

• The most important part of self driving car
• Did required image preprocessing
• data augmentation using Batch generator
• Fine tuning the model
• Used NVIDIA Neural model
• depolyed the model n Udacity self driving car simulator
• link - https://github.com/Sahana-M/self-driving-car/tree/master/Behavioural%20cloning%20-%20Nvidia%20neural%20model