Node Js,why so Asynchronous??!!

# Linear Regression, the basic Supervised Learning Algorithm

Let’s make it easy..:)

Linear Regression is one of the fundamental machine learning algorithms used to find the value of the dependent variable using the relationship between the independent and dependent variables.

In machine learning lingo, linear regression is a supervised learning algorithm used to predict the value of continuous output using the continuous dependent variable. In short, linear regression maps continuous x to a continuous y.

Linear regression is specifically used in predicting the continuous or real output. …

Machine learning, the future of new Technology

The future of predicting and analyzing the data

Well, machine learning, the term explains by the name itself, i.e. learning by the machine.
In fact, it is true that machine learning is the subset of Artificial Intelligence in which machine (in fact our computer) can learn by itself by feeding the data to it without explicitly programming by others, unlike other traditional programming languages. By traditional programming means that there we give input and there will be the function that will process the input and provides us our desired output. …

# ReLU Activation Function, Leaky-Relu

An activation function is a function that decides whether the nodes should be activated or not on the basis of weights and bias assigned to it. The activation function takes an input and gives a non-linear output if an activation function used for the model is a non-linear activation function like ReLU. Before talking about the ReLu activation function, let me talk about why it is popular in comparison to earlier non-linear activation functions like sigmoid and hyperbolic- tangent function, why ReLu is the most popular choice of deep learning today.

Well, sigmoid and tangent was very much popular non-linear…

# DropOut in DEEP LEARNING

An artificial Neural Network is a network of different inputs and hidden layers.

Neural Network is a very powerful model when it comes to solving very complex problems like Image Recognition, Speech Recognition. So, whenever the Artificial Neural Network is very deep then there is one problem. The problem is that the deeper the neural network has and the more the weights and bias it has, Artificial Neural Network tends to overfit the model. Overfitted model is a kind of problem as we may be confused from it if we did not analyze it properly. At first glance in the…

# Vanishing Gradient Problem in Deep Learning

Artificial Neural Network is actually not a new concept. However, it is true that its popularity is increasing now exponentially. The earlier neural network was coined in the 1950s however, initially, it was not much popular. It was developed but it failed to achieve what it was expected to do. The main reason behind this was the use of an Activation Function using sigmoid in each and every neuron. The problem using sigmoid as activation function created as a problem which was known as “Vanishing Gradient Problem”.

# Deep Learning

Deep learning is the subset of machine learning which is used for large datasets that even traditional Machine Learning algorithms fail to provide a certain level of good accuracy. The impact of Deep Learning is now increasing exponentially because most of the complex problem that was considered impossible is now made possible with the help of Deep Learning. Before diving into further let’s look back to its history.

Perceptron, in the Deep learning term a simple neural network was invented by Frank Rosenblatt in 1957. The concept was simple. Just train a model with some inputs with some wights. However…

Confusion Matrix, a useful metrics for Classification Model in Data Science and Machine Learning

Confusion Matrix is an evaluation matrix that is used to see where the model got confused, which is to see the false values where the model got confused.

In the confusion matrix, we can visualize where the data has missed predicting its true values. For that, we must know some terms about what is such parameters.

Follow are the important parameters that are associated with confusion matrix.

1. True Positive
2. True Negative
3. False Positive
4. False Negative

1.True Positive: These are the values or data points that were…

Numpy (Numerical Python), the backbone of all Data Science and Machine Learning

Numpy aka Numerical Python is the foundational base for all the data science and machine learning projects. Numpy is very similar to lists in python. But what makes it different from lists is that Numpy is 50 times faster than lists in python.

Arrays are very frequently used in data science, where speed and resources are very important. Since Numpy arrays are stored in memory unlike lists so processing and manipulating them is very efficient and fast.

The main purpose of the Machine Learning model is to train…

Cross-Validation in machine Learning, the way to improve the estimation of our result rather than just guessing it.

In machine learning, by default, we use an accuracy score to evaluate the measure of our study. It might give one accuracy result in one case and another result in another run. For instance, if you have noticed that if we take a random state value initially say 0, then it will give one output, and next time if we use random state value as 42, it gives another small changed value. …

## Santosh Thapa

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