Machine Learning and Deep Learning with Srinivas

Introduction In this paper, we will classify the handwritten digits using a multilayer neural network. We will use this classification to build the human verification system as we ask humans to write a 3-digit number and check if written correctly and validate the number entered by the user. As they are many ways to write some digits and they can be written anywhere in the box we use open CV to get the perfect size image and use ML to predict the number and their using JavaScript we verify the number. For the prediction, we are using a 3 hidden layer neural network. In the MNIST dataset, we get a 28*28 size image dataset in which each is surrounded by 4 pixels in every direction. We have achieved a 97.23% success rate of the classification of digits from the MNIST dataset. GitHub Live Demo Data Analysis and Visualization MNIST data consists of 70,000 handwritten digit images. We will follow the steps from preprocessing to predicting the digit. We will start by understanding how an ima...

Tensor : It is a mathematical object and  can be a number, vector, matrix, or an n-dimensional array. Padding : Increase the image size shape by adding the given  amount of pixels when it is being processed by the kernel of a CNN Stride : The value determines the kernel's jumping over how many pixels while moving the input. Max - Pooling : Decrease the height and width of the output tensor from each convolution layer by replacing the max of the block. Use of DataLoader : Split the dataset into batches of data of given size and also provides the utilities like shuffling, random sampling while forming a batch. Use of Validation set : Helps in evaluating the model during training i.e adjusting hyperparameters and pick the best version of the model. By this, we can also identify the occurrence of overfitting. Can accuracy be a loss function for a classification problem? No. The accuracy is not a differential function so we cant compute the gradients as there is no mathematical for...