Boosting Model Accuracy with Transfer Learning

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prerequisites
Intermediate Python • Intermediate TensorFlow/Keras • Understanding of transfer learning concepts
skills learned
Applying and fine-tuning pre-trained models • Comparing model architectures • Improving results with minimal training data
Satyajit Pattnaik
1 week · 6-8 hours per week · INTERMEDIATE
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Look inside

In this liveProject, you’ll work alongside EcoSanAI, a startup dedicated to wildlife monitoring and conservation. Their goal is to develop a smart wildlife monitoring system that can tell Asian and African elephants apart, helping track migration routes and conservation progress. To achieve this, you’ll develop a deep learning-based image classifier that uses transfer learning to accurately differentiate between Asian and African elephants. Begin by preparing a labeled dataset of Asian and African elephants with proper preprocessing and splits. Then select a pre-trained CNN like Xception or MobileNet, then adapt it for binary classification by adjusting the final layers and adding regularization. Train and fine-tune the model with callbacks to optimize performance, and finish by testing on a held-out set to evaluate accuracy and identify improvements.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Satyajit Pattnaik
Dr. Satyajit Pattnaik is a Lead Data/AI Architect with over 14 years of experience in software development, focusing on AI, data migration, and cloud computing. He has held key roles at PALO IT and Xccelerate, leading digital transformations and developing innovative Data and AI solutions. Dr.Pattnaik has served as a keynote speaker at international conferences and contributed to several publications and online courses in data analytics, data science and AI. He holds a Doctorate in Business Analytics and an MSc in Data Science, demonstrating his commitment to advancing the field.

prerequisites

The liveProject is for aspiring machine learning engineers, AI-curious developers, and students looking to dive deep into deep learning through a fun, real-world project.


TOOLS
  • Beginner Python
  • Beginner TensorFlow and Keras
  • Beginner Jupyter Notebook
TECHNIQUES
  • Basics of deep learning and neural networks
  • Basics of image processing
  • Basics of deep learning model evaluation

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