Artificial neural networks are computational models which are inspired by biological neural networks, and it is composed of a large number of highly interconnected processing elements called neurons.

An ANN (Artificial Neural network) is configured for a specific application, such as pattern recognition or data classification.

It can derive meaning from complicated or imprecise data.

It extracts patterns and detects trends that are too complex to be noticed by either humans or other computer techniques.

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Transfer Function

The behavior of ANN (Artificial Neural Network) depends on both the weights and the input-output function, which is specified for the unit. This function falls into one of these three categories:

Linear (or ramp)
Threshold
Sigmoid

Linear units: The output activity is proportional to the total weighted output in linear units.

Threshold: The output is set at one of two levels, depending on whether the total input is greater than or less than some threshold value.

Sigmoid units: The output varies continuously but not linearly as the input changes. Sigmoid units bear a more considerable resemblance to real neurons than do linear or threshold units, but all three must be considered rough approximations.

Below is the code by which we classify the neural network.

Firstly, we made an activation function so that we have to plot as POPC and to create the sigmoid function, which is an effortless activation function takes in Z to make the sigmoid.

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Then, we make the operation which inherits sigmoid. So let’s see a classification example and sikat learn has a helpful function and capabilities to create data set for us. And then we are going to say my data is equal to make blobs. It just creates a couple of blobs there that we can classify. So, we have to create 50 samples and the number of features to a status that’s going to make two blobs, so this is just a binary classification problem.

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Now, we have to create the scatterplot of features all the rows in column 0 and so if we do scatterplot of two distinctive blobs and able to classify these two highly separable classes.

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Here, we’re going to build a matrix of one that’s a matrix of one by two. And then, we pass that into our sigmoid function say sigmoid Z because that’s necessarily going to output is 0 or 1 for us as we’re classifying them based on whether it is positive or negative.

The more positive input, the more sure our model is going to be that it belongs to the one class.

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So now we were able to successfully use our graph objects placeholders’ variables activation functions to the recession and able to perform a very simple classification. And hopefully, soon we know how to do this manually it’s going to make learning tensor flow a lot and easier in performing all essential functions with the TensorFlow.

So, this brings us to the end of blog. This Tecklearn ‘Classification of Neural Network in Tensor Flow’ blog helps you with commonly asked questions if you are looking out for a job in Artificial Intelligence. If you wish to learn Artificial Intelligence and build a career in AI or Machine Learning domain, then check out our interactive, Artificial Intelligence and Deep Learning with TensorFlow Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/artificial-intelligence-and-deep-learning-with-tensorflow/

Artificial Intelligence and Deep Learning with TensorFlow Training

About the Course

Tecklearn’s Artificial Intelligence and Deep Learning with Tensor Flow course is curated by industry professionals as per the industry requirements & demands and aligned with the latest best practices. You’ll master convolutional neural networks (CNN), TensorFlow, TensorFlow code, transfer learning, graph visualization, recurrent neural networks (RNN), Deep Learning libraries, GPU in Deep Learning, Keras and TFLearn APIs, backpropagation, and hyperparameters via hands-on projects. The trainee will learn AI by mastering natural language processing, deep neural networks, predictive analytics, reinforcement learning, and more programming languages needed to shine in this field.

Why Should you take Artificial Intelligence and Deep Learning with Tensor Flow Training?

According to Paysa.com, an Artificial Intelligence Engineer earns an average of $171,715, ranging from $124,542 at the 25th percentile to $201,853 at the 75th percentile, with top earners earning more than $257,530.
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IBM, Amazon, Apple, Google, Facebook, Microsoft, Oracle and almost all the leading companies are working on Artificial Intelligence to innovate future technologies.

What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more

Real world projects in recommender systems and others

Got a question for us? Please mention it in the comments section and we will get back to you.

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