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Topic 22 | Neural Networks |

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  AI Free Basic Course | Lecture 22 | Neural Networks  In today lecture we will continue the Neural Network; in last lecture we gave the basic idea about what is to expect from neural network. Today we will explain the hyper parameters and its components. We will see the affect of the hyper parameter on the neural network. We will use playground and practice on notebook. At the end of lecture, we will implement a basic network using Tensor Flow. Today we will go through the things and would discuss the notebook in detail in tomorrow’s lecture that what does construct means in dense of Tensor flow, what does the layers do and what is loss function. What are possibilities to add in hidden layer and output layer. After practicing in TensorFlow you will get better idea about things. So, we will implement a neural network in TensorFlow and run it to see the outcome. Suppose we have few balls and our target is to through them in a basket placed at a distance of 10 meter. When ...
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AI Free Basic Course | Lecture 24 | Basics of Convolutional Neural Network (CNN)

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AI Free Basic Course | Lecture 24 | Basics of Convolutional Neural Network (CNN) In the last lecture importance and value of neural networks was discussed. We are targeting the high value projects having value above 30,000/-. It is advised that you must not directly into these projects. You need to set goals and move systematically towards it. You have to decide from where I am going to start. It can either be prompt engineering or any other field. In order to achieve your goal, allocate a specific portion of your day towards achievement of these goals. The way to move forward were asked by people from non-technical background. My response to them is that you need not understand everything. We must know the difference between functionalities of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) CNN). We must understand the reasons why the tasks that could not be performed by Artificial Neural Network (ANN) can be performed by Convolutional Neural Network (CNN) ( C...
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Topic 17 | Linear regression using Sklearn

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    AI Free Basic Course | Lecture 17 | Linear regression using Sklearn   Regression is the technique of supervised learning. There are two techniques in supervised learning, one is classification and other is regression. In classification we predict category / nominal value. Category or nominal value means something that has label; for example smoker /nonsmoker, cat /dog etc and we have to convert them into numerical value by effort. In prediction, we use the word positive and negative. For example, if a picture is given to the model and how it will predict whether it is picture of cat or not. Positive value in this would be that it is cat as we want to recognize cat. In medical test, the result is either positive or negative. So positive value is the value in which we are more interested to predict. Remember it does not depend upon  whether covid report is positive nor not, it depends what we have set the algorithm about which we are intereste...
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