BMS INSTITUTE OF TECHNOLOGY AND MANAGEMENT

Yelahanka, Bengaluru 560119

DEPARTMENT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Deep learning (BAI602) CCA - 1: Industry Integration Learning

“Exploration of DL Tool Boxes from MathWorks (MATLAB)”

Submitted by
NAME: SHASHANK TS
USN: 1BY22AI095

Under the guidance of

Prof. Sanjay M Belgaonkar
Assistant Professor
Dept. of AI & ML

2024 – 2025

EVEN SEMESTER
 CCA / AAT -1: Industry Integration Learning

Project Title: Exploration of DL Tool Boxes from MathWorks (MATLAB)

Tools and Technologies Used:

1. MATLAB:
Served as the primary environment for developing, visualizing, and evaluating deep learning models.
2. Deep Learning Toolbox:
Provided essential components such as customizable layers, training routines, pretrained models, and
utilities for building and training neural networks.
3. Image Processing Toolbox:
Utilized for image data preprocessing and augmentation, as well as for tasks involving visualization
and manipulation of image inputs.
4. Pretrained Networks:
Leveraged popular pretrained models like AlexNet, VGG, ResNet, and YOLO to perform
classification and object detection through transfer learning.
5. Image Labeler App:
An interactive MATLAB tool used for manually annotating objects within images, enabling the
preparation of datasets for object detection.
6. Training and Evaluation Utilities Key functions employed included:
• trainNetwork: For model training.
• trainingOptions: To specify training configurations and hyperparameters.
• evaluateDetectionPrecision: To measure the accuracy of object detection models.
• analyzeNetwork: To inspect and visualize the architecture of deep learning models.

1. Introduction:
In recent years, deep learning has revolutionized numerous industries, particularly in the realm of
image processing. From facial recognition and medical imaging to autonomous navigation and
industrial inspection, deep learning forms the backbone of modern visual intelligence systems. At
the heart of this transformation lie Convolutional Neural Networks (CNNs) — powerful
architectures renowned for their ability to extract and understand spatial features from image
data.
 This report summarizes the key tools, techniques, and practical insights gained from the
certification course, with a focus on applying deep learning to image-related challenges using
MATLAB.

2. Learning Objectives:
• To gain a solid understanding of Convolutional Neural Network (CNN) architectures and how
they process visual information.
• To develop the ability to visualize and interpret the internal operations of deep neural networks.
• To learn how to fine-tune training parameters and monitor model performance during training.
• To understand how to apply deep learning techniques to regression tasks.
• To acquire practical skills in building and evaluating object detection models using transfer
learning methods.
3. Overview of Completed Courses:
The certification path comprised four specialized courses, each addressing a distinct area of deep
learning in MATLAB. A summary of each course is provided below:

i. Explore Convolutional Neural Networks: This course introduced the fundamental structure
and functionality of CNNs, with an emphasis on interactive visualization and hands-on
experimentation.
• Covered the role of filters, activations, and feature maps in image interpretation.
• Explained how data flows through the network, layer by layer.
• Enabled experimentation with pretrained models to understand classification workflows.

ii. Tune Deep Learning Training Options: Designed for those familiar with the basics of training
networks, this course focused on customizing the training process for improved performance.
• Explored training parameters such as learning rate schedules, batch sizes, and early
stopping.
• Emphasized the importance of monitoring training progress using validation metrics.
• Shared best practices for avoiding overfitting and optimizing learning efficiency.

iii. Regression with Deep Learning: This course extended the application of deep learning to
predict continuous outputs, moving beyond classification.
• Demonstrated how to modify both input data and network architecture for regression
tasks.
• Discussed suitable loss functions and evaluation metrics for continuous predictions.
• Included practical examples such as age estimation and value prediction.
 iv. Object Detection with Deep Learning: The final course focused on object detection, using
transfer learning and modern architectures like YOLO.
• Covered multi-class detection, including training models on custom-labeled data.
• Walked through the complete workflow: image labeling, data splitting, training, and
evaluation.
• Introduced post-processing techniques to refine predictions for real-world applications.

4. Certificate Significance:
The certification in Deep Learning Techniques in MATLAB for Image Applications signifies the
successful completion of a structured, hands-on learning path focused on applying deep learning to
complex image processing tasks. It reflects both theoretical understanding and practical proficiency in
core areas such as convolutional neural networks (CNNs), regression models, and object detection
using transfer learning.
Beyond traditional image classification, the course emphasized real-world workflows including training
configuration, model validation, and performance fine-tuning, all within the MATLAB environment.
Earning this certificate demonstrates the ability to effectively develop, train, and deploy deep learning
models for a range of vision-related challenges using industry-relevant tools.

5. Conclusion:
Completing the Deep Learning Techniques in MATLAB for Image Applications course provided a
comprehensive introduction to applying deep learning across various image-based use cases. Through
in-depth work with CNNs, learners gained practical insight into how networks extract features and
process visual data across layers to perform classification.

The course also extended into regression applications, where deep learning is used to predict
continuous values rather than discrete categories. This is particularly useful in tasks such as estimating
object size, predicting quality scores, or analyzing numerical trends in visual data.
A significant component was dedicated to object detection, with a focus on the YOLO architecture for
real-time, multi-class detection. Learners were introduced to the full pipeline—from data labeling and
training-validation splitting to evaluation and post-processing—along with techniques for adapting
pretrained models to custom datasets using transfer learning.

By the end of the course, learners had developed skills in training, tuning, and evaluating deep learning
models for practical deployment. The hands-on MATLAB environment ensured familiarity with tools
and workflows used in real-world projects, making learners well-prepared to contribute to fields such as
healthcare imaging, autonomous systems, industrial inspection, and security.
Shashank TS
Shashank TS

TS
Shashank TS
Shashank TS
Shashank TS