Call Us

Home / Blog / Data Science Digital Book / Multi-Layered Perceptron (MLP) / Artificial Neural Network (ANN)

Multi-Layered Perceptron (MLP) / Artificial Neural Network (ANN)

  • July 15, 2023
  • 4928
  • 26
Author Images

Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

Read More >

Non-Linear patterns can be handled in two ways:

Changing Integration Function:

multi layered perceptron

The nonlinear pattern will not be captured by the mere existence of hidden layers. The activation function that will be employed must not be linear.

Only linear patterns may be captured by using linear or identifiable activation functions inside the neurons of the hidden layer.

Learn the core concepts of Data Science Course video on YouTube:

A linear activation function is assumed by default by the network if no activation functions are provided in the layers.

multi layered perceptron

360DigiTMG offers the Artificial Intelligence Coaching Institutes in Bangalore to start a career in AI. Enroll now!

List of Activation Functions Include

multi layered perceptron

Regularization Techniques used for Overfitting

L1 regularization / L1 weight decay term

L2 regularization / L2 weight decay term

multi layered perceptron

Want to learn more about AI? Enroll in this Artificial Intelligence Coaching Institutes in Hyderabad to do so.

Error-Change Criterion

  • Stop when error isn't dropping over a window of, say, 10 epochs
  • Train for a fixed number of epochs after criterion is reached (possibly with lower learning rate)

Artificial Intelligence is a promising career option. Enroll in the Masters in Artificial Intelligence Program offered by 360DigiTMG to become a successful Artificial just a step away. Check out the Artificial Intelligence Course at 360DigiTMG and get certified today

Weight-Change Criterion

  • Compare weights at epochs t-10 & t and testmulti layered perceptron
  • Possibly express as a percentage of the weight

Also, check this Artificial Intelligence Coaching Institutes in Pune to start a career in Artificial Intelligence. Looking forward to becoming a Artificial Intelligence expert? Check out the Artificial Intelligence Course and get certified today.


This method of model averaging in the Deep Learning Training Phase is interesting: Ignore (zero out) a random subset, p, of nodes (and associated activations) for each hidden layer, training sample, and iteration.

In the test phase, use all activations, but scale them down by a factor p (to make up for the activations that were not present during training).

Artificial Neural Network (ANN)

Choose a selection of nodes at random, then reduce their output to zero.

Randomly select a subset of nodes and force their output to zero.

multi layered perceptron

Drop Connect

But unlike dropout, we deactivate the weights rather than the nodes. The nodes are a little bit active here.

multi layered perceptron

Watch Free Videos on Youtube


multi layered perceptron

360DigiTMG the award-winning training institute offers a Artificial Intelligence Coaching Institutes in Pune and other regions of India and become certified professionals.

Batch Normalization:

Input: Values of x over a mini-batch: B = { x1...m };

multi layered perceptron

  • Batch Normalization layer is usually inserted before non-linearity layer (after Fully Connected or Dense Layer)
  • Reduces the strong dependence on weight initialization

Shuffling Inputs

  • Choose examples with maximum information content
  • Shuffle the training set so that successive training examples never (rarely) belong to the same class
  • Present input examples that produce a large error more frequently than examples that produce a small error. Why? It helps to take large steps in the Gradient descent

Weight Initialization Techniques:

Xavier’s initialization
multi layered perceptron
Caffe implements a simpler version of Xavier’s initialization
caffe implements
He’s initialization
he's initialization

Data Science Placement Success Story

Data Science Training Institutes in Other Locations

Navigate to Address

360DigiTMG - Data Analytics, Data Science Course Training Hyderabad

2-56/2/19, 3rd floor, Vijaya Towers, near Meridian School, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081

099899 94319

Get Direction: Data Science Course

Make an Enquiry