TIRUMALA ENGINEERING COLLEGE
Affiliated to Jawaharlal Nehru Technological University Kakinada
Approved by AICTE and Accredited by NAAC & NBA
Jonnalagadda, Narasaraopet, PIN: 522601
PYTHON: DEEP LEARNING
COMPUTER SCIENCE AND ENGINEERING
CERTIFICATE
This is certify that this is the bonafide record work done
by Mr/Ms. Yallapragada Aparna Regd.Of 22NE1A05J1 Of IV year B.Tech I
semester in the Python:DeepLearning Laboratory during the academic year
2025-2026 Performed 12. Number of experiments out of 12.
Lab-in-charge Head of the Department
Internal Examiner External Examiner
INDEX
S.NO DATE NAME OF THE EXPERIMENT PAGE NO MARKS SIGNATURE
EXERCISE-1
MODULE: Build a Convolution
Neural Network for Image
1 Recognition.
EXERCISE: Go through the
modules of the course mentioned
and answer the self-assessment
questions given in the link below at
the end of the course.
EXERCISE-2
MODULE: Understanding and
2 Using ANN: Identifying age group
of an actor.
EXERCISE: Design Artificial
Neural Networks for Identifying and
Classifying an actor using Kaggle
Dataset.
EXERCISE-3
MODULE: Understanding and
3 Using CNN: Image recognition.
EXERCISE: Design a CNN for
Image Recognition which includes
hyperparameter tuning.
EXERCISE-4
4 MODULE: Predicting Sequential
Data.
EXERCISE: Implement a
Recurrence Neural Network for
Predicting Sequential Data.
EXERCISE-5
MODULE: Removing noise from
5 the images.
EXERCISE: Implement Multi-Layer
Perceptron algorithm for Image
denoising hyperparameter tuning.
EXERCISE-6
MODULE: Advanced Deep
6 Learning Architectures.
EXERCISE: Implement Object
Detection Using YOLO.
EXERCISE-7
7 MODULE: Optimization of Training
in Deep Learning
EXERCISE: Exercise Name: Design
a Deep learning Network for Robust
Bi-Tempered Logistic Loss.
EXERCISE-8
8 MODULE: Advanced CNN.
EXERCISE: Build AlexNet using
Advanced CNN.
EXERCISE-9
MODULE: Autoencoders Advanced.
9 EXERCISE: Demonstration of
Application of Autoencoders.
EXERCISE-10
MODULE: Advanced GANs
10 EXERCISE: Demonstration of GAN
EXERCISE-11
11 MODULE: Capstone project
EXERCISE: Complete the
requirements given in capstone
project
EXERCISE-12
12 Module name: Capstone project
Exercise : Complete the
requirements given in capstone
project
Course Outcomes:
At the end of the Course, Student will be able to:
• Demonstrate the basic concepts fundamental learning techniques and layers.
• Discuss the Neural Network training, various random models.
• Apply various optimization algorithms to comprehend different activation
• functions to understand hyper parameter tuning
• Build a convolutional neural network, and understand its application to build a
• recurrent neural network, and understand its usage to comprehend auto encoders
to briefly explain transfer learning
CO/
PO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
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CO6 - - 3 2 3 - - - 2 - - -
