Implementation of Neural Network in TensorFlow

Last updated on Oct 24 2021
Goutam Joseph

Table of Contents

Implementation of Neural Network in TensorFlow

Neural Network is a fundamental type of machine learning. It follows the manual Ml workflow of data preprocessing, model building, and model evaluation.

We will be going to start object-oriented programming and the super keyword in Python.

Now let’s launch Jupiter notebook in our system through Anaconda Navigator.

Firstly, we have to open Anaconda Navigator, and from there we have to launch the Jupyter Notebook.

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navigator

After that, a command will run in our system automatically like below.

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jupyter notebook

Then, the below page will open where we have to code.

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directory page

Then click on the files and start the editor.

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editor

Now, it will successfully open in our system.

Then we have to understand simple functions in our coding section below:

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coding section
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coding section2
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coding section3

Operators

Following are the basic concept of operators:

  • Operation Class
    • Input Nodes
    • Output Nodes
    • Global Default Graph Variable
    • Compute
      • Overwritten by extended classes
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operation
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operation2

Graph

A graph is a global variable. We are using this term graph is because tensor flow runs off graphs and we will reiterate it when we learn about the TensorFlow basics in the next section. We can imagine a graph as kind of the list of nodes. And in this case, we have this simple graph where we have two constants are two nodes n1 and n2 and each of those is a constant 1 and 2 respectively and then it is feeding into some operation. So in our case, we have kind of this selection operation class, and then this operation class is going to be inherited by other classes.

So, for example, we can add a class that inherits operation class, and in that case, it takes in those two inputs 1 and 2 and then outputs three because of 1+2=3. We have a multiply operation so that multiply operation is saying one time two, so the output is 2. Below is the graph.

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multiply operation

Placeholder- An “empty” node that needs a value to be provided to compute output.

Variables- It is a changeable parameter of the graph.

Graph- Global Variable connecting variables and placeholders for operations.

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graph
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graph2

Session: We need to execute all the operations in a meeting. We will make sure we should implement the nodes in the correct order.

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session
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session2
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result
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run
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final

Here, we are done with our operators and graph section.

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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

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