Category:
Deep Learning
Difficulty:
Intermediate
Prerequisite(s):
Python, TensorFlow or PyTorch for deep learning
Skills to be Learned:
Building and training CNN models for binary classification (cat vs. dog).
Cat vs Dog Classification using CNN
Dive into the captivating realm of computer vision and deep learning with our hands-on course. Learn the art of image classification by distinguishing between cats and dogs using Convolutional Neural Networks (CNNs). Gain the expertise to build, train, and evaluate CNN models, setting the stage for tackling real-world computer vision challenges.
This course is a hands-on program designed for individuals interested in computer vision and deep learning. In this course, participants will dive into the fascinating world of image classification, specifically focused on distinguishing between cats and dogs in images. Students will learn how to build Convolutional Neural Network (CNN) models, a powerful class of deep learning models for image-related tasks, and apply them to real-world scenarios. By the end of the course, participants will have the knowledge and skills to create their own image classification models and tackle similar computer vision challenges.
Learning Outcomes:
Upon completing this course, participants will:
- Gain a solid understanding of computer vision concepts and image classification.
- Master the Python programming language for deep learning applications.
- Become proficient in creating Convolutional Neural Network (CNN) architectures.
- Learn data preprocessing techniques for image data.
- Build and train CNN models for binary classification (cat vs. dog).
- Apply transfer learning using pre-trained CNN models like VGG16 or ResNet.
- Evaluate model performance and use relevant metrics.
- Understand best practices for data augmentation and handling imbalanced datasets.
- Be equipped to work on image classification projects beyond the scope of this course.
Prerequisites:
- Basic programming knowledge, preferably in Python.
- Familiarity with fundamental machine learning and deep learning concepts is helpful but not required.
- Access to a Python development environment with libraries such as TensorFlow or PyTorch for deep learning, NumPy for data manipulation, and Matplotlib for visualization.
Libraries and Programming Language Used:
- Python for coding and scripting.
- TensorFlow or PyTorch for building and training deep learning models.
- NumPy for data manipulation.
- Matplotlib for data visualization.
Course Syllabus:
Introduction to Image Classification and CNNs
   - Overview of image classification tasks.
   - Understanding the architecture and components of Convolutional Neural Networks (CNNs).
Setting Up the Development Environment
   - Installing Python and required libraries.
   - Preparing the development environment for deep learning projects.
Cat vs. Dog Dataset
   - Introduction to the dataset.
   - Data exploration and visualization.
Data Preprocessing
   - Data loading and transformation.
   - Data augmentation techniques to enhance the dataset.
Building a CNN Model
   - Designing a CNN architecture for binary classification.
   - Configuring and compiling the model.
Training the Model
   - Preparing the dataset for training and validation.
   - Training the CNN model.
   - Monitoring training progress and avoiding overfitting.
Model Evaluation and Metrics
   - Assessing model performance using accuracy, precision, recall, and F1-score.
   - Confusion matrix and ROC curves for binary classification.
   - Making predictions
this course immerses participants in the world of image classification, with a specific focus on distinguishing between cats and dogs in images. Students will unravel the intricacies of building Convolutional Neural Network (CNN) models, a potent class of deep learning models tailored for image-related tasks. These skills will be applied to practical, real-world scenarios, equipping participants with the knowledge and expertise to create their own image classification models and tackle similar computer vision challenges.