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...

Introduction The entire aim of optimization is to minimize the cost function . We will learn more about optimization in the later sections of the paper. Batch Gradient Descent Here we sum up all examples on every iteration while performing the updates for the weight or parameters. So for every update in weights, we need to sum over all examples. The weights and bias get updated based on gradient and learning rate(n). Mainly advantages when there is a straight trajectory towards minimum and it has an unbiased estimate of gradients and fixed learning rate during training. Disadvantageous when we use vector implementation because we have to go over all the training set again and again. Learning happens when we go through all data even when some examples are reductant and with no contribution to the updating. Stochastic Gradient Descent  Here unlike Branch Gradient Descent, we update the parameters on each example so learning happens on every example. So it converges more quickly than...

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...

  This is a continuation of  this Loss Functions used for Classification  As we all know that for regression problems we use Least square error as the loss function. Through this, we get a convex loss function and we can optimize by finding its global minimal. But when it comes to logistic regression the concept is completely changed, the Least Square error will give us a non-convex loss function with, more than one local minima. Here we get a wavy curve due to the non-linear sigmoid function used in the logistic regression hypothesis so it has multiple local minima which are bad for gradient Descent which is used to find minima.   Cross-Entropy Loss   This is the most common setting for classification problems. Cross-entropy loss increases as the predicted probability diverge from the actual label. An important aspect of this is that cross-entropy loss penalizes heavily the predictions that are confident but wrong . We can’t give equal weight to all false resul...

Introduction  First let us understand, how the machine learns from the given data. Actually, it is learning the relationship within the data. There are 3 steps in which the machine learns first it will predict an output. Mainly the first prediction is mostly random. Then it calculates the error and then learns and then this process happens many times. The error goes on reducing cost function is also known as loss function. If the cost functions are convex, then it is easier to calculate the error and minimize it as the global and local minima. But not all cost functions are convex in nature. We will understand the error functions slowly by looking at examples by observing their graphs.  Loss Functions  Why we need Loss Functions?  Loss function actually measures how good a prediction the result/outcome made by the model is so, it’s a measure of how good is the mode l.  Is Cost Function the same as the Loss function?  In our day-to-day ...