Neural Network Agent

This agent simply takes current state as an input and predict the best possible move which can make agent win. 

Neural Network is made up of 4 hidden layers including one input layer and one output layer. Each layer is having 26 neurons in it and output layer have 7 neurons. This 7 neurons represents columns of a board and Neural network gives best possible column to play move.  Softmax_cross_entropy is used for cost estimation and AdamOptimizer for optimization of cost of neural network while back propagating. Neural Network is trained using random rollout agent played against another one with different walks. Model is trained on 150,000 games which are won by AI agent.

This neural network returns vector of size 7 which contain probability values for each column and we can select highest one to make it closer to win.


We have developed an Artificial Intelligence model which is one of the best applications of AI (Artificial Intelligence) for pharmaceutical companies. This model take drug names as inputs and according to chemical and biological features and examining different types of drugs like enzymes, chemical etc. It also predicts side effects which are associated with new drugs which can be made from two or more drugs. So, let’s see some insight about this model. How this model is working and what are the features and concepts are used to make this model work in an efficient way.

Data set used for AI model

This model is built using deep neural networks which is multi-perceptron model. Here by every neuron we mean some kind of function and activation function for that layer. These neurons are inspired by human brain and learns new things in the same way.

For training our model of neural nets data of 832 Medicines is used where each medicine have 40260 features. We have used two hidden layers in our program which 2200 and 202 neurons in each layer consecutively.

OpenCV is the most popular library for computer vision. Originally written in C/C++, it now provides bindings for Python.

OpenCV uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns and features that must be matched.


Though the theory may sound complicated, in practice it is quite easy. The cascades themselves are just a bunch of XML files that contain OpenCV data used to detect objects. You initialize your code with the cascade you want, and then it does the work for you.

Before we jump into the process of face detection, let us learn some basics about working with OpenCV. In this section we will perform simple operations on images using OpenCV like opening images, drawing simple shapes on images and interacting with images through callbacks. This is necessary to create a foundation before we move towards the advanced stuff.

In my program, I have taken some images for training my model on the images of particular person with different orientations.

I built the program in three stages which are :

  1. Data Creator or generator
  2. Trainer
  3. Predictor