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Deep Learning




NumPy, TensorFlow, or PyTorch.

Skills to be Learned:

In-depth knowledge of Artificial Neural Networks and their components.

Image Classification on MNIST Dataset (Using ANN)

Kickstart your journey in deep learning with this project-based course, focusing on image classification using Artificial Neural Networks (ANNs) and the well-known MNIST dataset.

About the course:

This course is an ideal starting point for individuals interested in deep learning. This project-based course is designed to introduce participants to the fundamentals of image classification using Artificial Neural Networks (ANNs). The MNIST dataset, a collection of hand-written digits, serves as a practical and well-known platform for this exploration. 

Throughout the course, students will gain hands-on experience in preprocessing image data, building ANN models, training classifiers, and evaluating model performance. By the end of the course, participants will have the skills to develop their image classification models and understand the foundations of neural network-based image analysis.

Learning Outcomes:

Upon completing this course, participants will:

- Understand the basics of image classification and its applications.

- Develop proficiency in Python programming for machine learning.

- Master data preprocessing techniques for image data.

- Learn the architecture and components of Artificial Neural Networks (ANNs).

- Build, train, and fine-tune ANN models for image classification.

- Apply techniques for model evaluation and selection.

- Gain insights into the MNIST dataset and its relevance in computer vision.

- Be equipped to tackle image classification tasks in real-world scenarios.


- Basic programming knowledge, preferably in Python.

- Familiarity with fundamental machine learning concepts.

- Access to a Python development environment with libraries such as NumPy, TensorFlow, or PyTorch for deep learning.

Libraries and Programming Language Used:

- Python for coding and scripting.

- TensorFlow or PyTorch for building and training Artificial Neural Networks.

- Common libraries like NumPy for data manipulation.

Course Syllabus:

Introduction to Image Classification

   - Understanding image classification tasks and their importance.

   - Applications of image classification in various domains.

Setting Up the Development Environment

   - Installing and configuring Python, TensorFlow/PyTorch, and relevant libraries.

   - Preparing the development environment for image classification projects.

Introduction to MNIST Dataset

   - Overview of the MNIST dataset.

   - Exploring the dataset structure and characteristics.

Data Preprocessing for Image Classification

   - Loading and visualizing MNIST images.

   - Data normalization and preprocessing techniques.

Artificial Neural Networks (ANNs)

   - Fundamentals of ANN architecture.

   - Understanding layers, neurons, and activation functions.

Building an Image Classification Model

   - Designing and implementing a basic ANN for image classification.

   - Model compilation and configuration.

Training and Optimization

   - Preparing training and validation datasets.

   - Training the ANN model on MNIST images.

   - Hyperparameter tuning and optimization.

Model Evaluation and Metrics

   - Assessing model performance using accuracy, precision, recall, and F1-score.

   - Confusion matrix and ROC curves.

This course provides an excellent entry point for those eager to explore deep learning, particularly in the realm of image classification. It's structured around practical, project-based learning, using the MNIST dataset of hand-written digits as a foundation. Participants will engage in a hands-on experience, learning to preprocess image data, construct ANN models, train classifiers, and assess model performance. By the course's conclusion, students will be equipped with the skills to create their image classification models and a thorough understanding of neural network-based image analysis.
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