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Introduction
Automated recognition of handwritten digits is an essential task in contemporary technology, with applications in financial institutions, postal services, and many other areas. Our goal is to create a neural network model in this project that can recognize handwritten numbers from 0 to 9. To accomplish this, we’ll make use of neural networks and machine learning, with the MNIST dataset serving as our standard.
Libraries Used
For this project, we’ll harness the capabilities of the following Python libraries:
NumPy : A fundamental package for scientific computing in Python, providing support for multidimensional arrays and mathematical functions.
TensorFlow : An open-source machine learning framework developed by Google, offering comprehensive tools for building and training neural networks.
Matplotlib : A versatile plotting library for creating visualizations in Python, allowing us to analyze and visualize our data and model performance.
Outline
Packages: Introduction to the libraries used in the project and their significance in machine learning and neural network development.
ReLU Activation: An explanation of the advantages and applications of the Rectified Linear Unit (ReLU) activation function in neural networks.The below figure gives us an idea about common activation functions
Softmax Function: Detailed discussion of the softmax function for multiclass classification, including its mathematical formulation and application in neural networks. A multiclass neural network generates N outputs. One output is selected as the predicted answer. In the output layer, a vector 𝐳 is generated by a linear function which is fed into a softmax function. The softmax function converts 𝐳 into a probability distribution as described below.
And the response of the softmax function is shown in the graph below.
Neural Networks: Thorough investigation into developing a neural network for handwritten digit identification. It can be understood by the below figure
Problem Statement: The current aim is to train a neural network to identify handwritten numbers from grayscale pictures. Building a model that can accurately classify each image into its appropriate digit class is the aim, given a dataset of 28×28 pixel images representing digits 0 to 9.
Model Representation: An explanation of the neural network design, as shown in the diagram above, including the quantity of layers, units, and activation functions.
Softmax Placement: An explanation of the significance of softmax placement and how it affects model training and assessment. To achieve numerical stability, Softmax should be positioned with the loss function rather than the output layer.
Building the Model: With explanations and code snippets, a practical demonstration of building the neural network model in TensorFlow is provided.
Training and Evaluation: Comprehensive guidelines for using the MNIST dataset to train the model, assessing its performance, and deciphering the outcomes.
Results and Analysis: Evaluation of the accuracy, loss, and possible improvement areas of the model using performance indicators.
Conclusion: An overview of the main conclusions, learnings from the study, and applications of automated handwritten digit identification in several industries.
We set out to use TensorFlow and Python to create a neural network for handwritten digit recognition in this blog article. We illustrated how to build, train, and assess a model that can reliably recognize handwritten digits by utilizing the capabilities of neural networks and machine learning. Such models have the enormous potential to transform a number of real-world applications, such as automated mail sorting and check digit recognition in financial systems, with more refinement and optimization.
Git repo → https://github.com/YaxLU/NeuralNetwork.git