## What are Machine Learning Prerequisites and Machine Learning Terminologies for Beginners?

If you are keen to know the theory behind the algorithms and how they work, Knowing Below mentioned mathematical chapters and having knowledge of Python programming language is advantageous. Also Read:What is Machine Learning in Artificial Intelligence and What are it’s applications?

## Dataset Division,Model fit,Model Indicators, Feature Engineering in Machine Learning

Division of data sets: Training set – Learn the sample data set and build a model by matching some parameters, mainly for training the model. An analogy to the problem solving before the postgraduate study. Validation set – For the learned model, adjust the parameters of the model, such as selecting Read more…

## Supervised learning,Unsupervised learning and Reinforcement learning in Machinelearning

Classification: Divide instance data into appropriate categories. Application example: Determine whether the website is hacked (two classifications), automatic recognition of handwritten digits (multi-classification) Regression: mainly used to predict numerical data. Application examples: forecasting of stock price fluctuations, housing price forecasts, etc. Supervised learning: The value of the target variable must Read more…

## What is Machine Learning in Artificial Intelligence and What are it’s applications?

What is Machine Learning? Machine Learning is the use of computers to highlight the true meaning of the data, in order to convert the unordered data into useful information. It is a multi-disciplinary subject involving many disciplines such as probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. Specializing in Read more…

## Various types of GANs ,Evaluation Metrics of GANs

Issues in Training: Generator Diminished Gradient: Discriminator gets too successful that the generator gradient vanishes and learns nothing. As seen earlier the training of a GAN is typically alternating between Gradient Ascent on Discriminator and Gradient Descent on Generator. But optimizing GD on generator doesn’t work well. Consider the above Read more…

## what is Generative adversarial network (GAN)

GAN are Generative Adversarial networks. GANs are neural networks that generate synthetic data given certain input data. The main goal is unsupervised sampling from complex high dimensional distribution. And this is done by taking samples from random noise and learn the transformation to input distribution

## DeepFace vs Facenet for face recognition

Introduction: Face Recognition problems can be broadly classified into two categories ⦁ Face Verification: Identifying if the given face is of the claimed person ⦁ Face Recognition: Identifying different instances (of faces) of the claimed person Other type of problems includes Clustering (grouping similar faces together) Simple Techniques for face Read more…