Estimators, Bias, and Variance

Estimators, Bias and Variance

Introduction The field of statistics provides us with a lot of tools that may be used to attain the Machine Learning goal of resolving a task. That is not only helpful for the training set then likewise to take a broad view. Introductory concepts for example parameter estimation, bias and variance are valuable to strictly … Read more

Gradient descent Method in Machine Learning

Gradient descent Method in Machine Learning

Introduction Many deep learning models pick up objectives using the gradient-descent method. Gradient-descent optimization needs a big number of training samples for a model to converge. That creates it out of shape for few-shot learning. We train our models to learn to achieve a sure objective in generic deep learning models. However, humans train to … Read more

Overflow and Underflow in Deep Learning

Overflow and Underflow in Deep Learning

Introduction Deep learning algorithms generally need a high volume of numerical computation. This normally states to algorithms that solve mathematical problems. That is solved by methods to keep informed guesses of the solution through an iterative process. Somewhat than logically deriving a formula in case a symbolic expression for the correct solution. The general operations … Read more

Numerical Analysis

Introduction Numerical analysis is the learning of algorithms. It is the area of mathematics and computer science. That use numerical approximation to solve problems of mathematics. It makes, analyzes, and implements algorithms. Illustrations of numerical analysis comprise: Normal differential equations as found in celestial mechanics Forecasting the motions of planets, stars and galaxies Numerical linear … Read more

Deep learning for text and sequences

Deep learning for text and sequences

Introduction Deep-learning models that would process text either understood as sequences of word or sequences of characters, statistic, and sequence data generally. The two important deep-learning algorithms for sequence processing are recurrent neural networks and 1D convnets. We’ll discuss both of those approaches. Applications of those algorithms include the following: Document classification and statistic classification, … Read more

What is the Performance Measure, Learning algorithm?

Introduction In order to gauge the skills of a machine learning algorithm, we must design a quantitative measure of its performance. Usually, this performance measure P is restricted to task T being administered by the system. For tasks like classification, classification with missing inputs, and transcription, we frequently measure the accuracy of the model. Accuracy is simply the proportion of examples that the model produces the right output.  Description We will also … Read more

How to Avoid Overfitting in Deep Learning Neural Networks?

Introduction I will present five techniques to stop overfitting while training neural networks. 1. Simplifying The Model The first step when handling overfitting is to decrease the complexity of the model. We will simply remove layers or reduce the number of neurons to form the network smaller to decrease the complexity, While doing this, it’s … Read more