Requirements
- A computer with standard specs (Any operating system - Windows, Mac, Linux all are OK.)
- Basic Python Programming Language is preffered but not must if you know any other programming language
Project Outline/Description
Handwritten digit recognition is a very important task in the field of computer vision and deep learning. It is a process of recognizing handwritten digits from a given image. This task involves training a model on a dataset of images of handwritten digits, so that it can accurately
classify them. Deep learning algorithms such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are widely used for this purpose.
In our training, we will use well known datasets that are widely used by high level education about Machine Learning as well as custom datasets. By doing our projects, you will master artificial intelligence concepts as well as learn these famous datasets. After completing the course, you will be able to easily produce solutions to the problems that you may encounter in real life.
In our Machine Learning Projects we will use Scikit-Learn Python library.
In our Deep Learning Projects we will use Tensorflow and Keras libraries.
What you will learn?
This project is focused on using machine learning and deep learning algorithms to build a system that can recognize handwritten digits. Students who complete this project should be able to:
- Understand the basics of machine learning and deep learning, including how to train and evaluate models using these techniques.
- Familiarize themselves with the MNIST dataset, which is a common dataset used for training and evaluating models for handwritten digit recognition.
- Develop a machine learning model and a deep learning model for recognizing handwritten digits, and compare their performance.
- Gain practical experience implementing and evaluating machine learning and deep learning models using a real-world dataset.
- Understand the strengths and limitations of different machine learning and deep learning algorithms, and be able to choose appropriate algorithms for a given task.
- Understand how to use various tools and libraries (such as Tensor Flow) to build and train machine learning and deep learning models.
- Understand the importance of pre-processing and cleaning data before training a model. This could include techniques such as scaling, normalization, and handling missing or incorrect values.
- Develop an understanding of how to optimize model hyper parameters in order to improve performance. This could include techniques such as grid search, random search, or Bayesian optimization.
- Understand the concept of overfitting and be able to use techniques such as regularization and early stopping to prevent overfitting in their models.
