AI Saturdays Damascus… Working on a data set with models built from scratch
AI6 Damascus is going on in the Incubator of Communication Technology(ICT) where AI pensioners meet, talk and learn about deep learning every Saturday.
In our first session, we discussed the current state of artificial intelligence industry in our country and how rarely it is to find a real position in AI. That happens because of the impact of conflict on business where Syria is still in a chaos. That certainly prevent business enterprises from being involved in such an emerging field like Artificial Intelligence. In addition, few people in AI-related disciplines who take build their application based on AI approaches by themselves and it’s not an easy thing to do.
So we set a goal to create a Syrian AI community that aims to help startups implement any idea locally which make a big growth in this field and revive it again.
Recently, we’ve finished the first two courses of Andrew Ng’s deep learning specialization on Coursera. We were working collectively on studying the courses’ materials throughout the week to discus them in our meeting on Saturdays. We study, learn, discuss and come up with the ideal solutions together to apply the course’s concepts with a much more practical point of view.
Our Meeting In a Minute
We started with the basics by applying Logistic Regression and Neural Networks from scratch and then we downloaded a data set from Kaggle called ‘Social Network Analysis’ you can find it here
We did some pre-processing like encoding the Gender feature and scaling all features with Min-Max Scaler from scikit-learn
We have implemented the models without scaling and the cost function always returns a NaN(Not a Number) because the of the Log so it was very necessary here to scale the features.
we’ve split the data also in two ways, one is by using arrays slicing and the other by using train_test_split method in scikit-learn
Finally, we adjusted the shape of feature to be [#features, # instances]
and also the ground truth vector ‘Purchased’ because Prof. Andrew supposed in his Logistic Regression Implementation supposed that.
After implementing the Logistic Regression we applied the Neural Network model on the data in the same way.
P.S Anyone can find our full Jupyter Notebooks for the second and third session in these links:
