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Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages

Deep Learning for Image Classification
( 30 Modules )

Module #1
Introduction to Deep Learning
Overview of deep learning, its applications, and importance in image classification
Module #2
Image Classification Basics
Fundamentals of image classification, including types of classification problems and evaluation metrics
Module #3
Introduction to Convolutional Neural Networks (CNNs)
Overview of CNNs, their architecture, and how theyre used for image classification
Module #4
Deep Learning Frameworks
Introduction to popular deep learning frameworks such as TensorFlow, PyTorch, and Keras
Module #5
Data Preprocessing for Image Classification
Techniques for preprocessing images, including data augmentation, normalization, and feature scaling
Module #6
Building a Simple CNN
Hands-on exercise building a simple CNN using a deep learning framework
Module #7
Activation Functions and Optimization
Overview of activation functions and optimization techniques used in deep learning
Module #8
Convolutional Layers
In-depth look at convolutional layers, including types of convolutions and filter visualization
Module #9
Pooling Layers
Overview of pooling layers, including max pooling and average pooling
Module #10
Batch Normalization and Regularization
Techniques for improving model performance, including batch normalization and regularization
Module #11
Transfer Learning
Using pre-trained models and fine-tuning for image classification tasks
Module #12
Object Detection
Introduction to object detection, including techniques and architectures such as YOLO and SSD
Module #13
Image Segmentation
Overview of image segmentation, including semantic and instance segmentation
Module #14
State-of-the-Art Models
Overview of state-of-the-art models for image classification, including ResNet, Inception, and DenseNet
Module #15
Ensemble Methods
Techniques for improving model performance using ensemble methods, including bagging and boosting
Module #16
Handling Class Imbalance
Techniques for handling class imbalance in image classification datasets
Module #17
Model Evaluation and Selection
Metrics and techniques for evaluating and selecting the best model for an image classification task
Module #18
Real-World Applications
Real-world applications of deep learning for image classification, including self-driving cars and medical imaging
Module #19
Case Study:Image Classification with Deep Learning
Hands-on exercise working on an image classification project using deep learning
Module #20
Common Challenges and Solutions
Common challenges faced in deep learning for image classification and their solutions
Module #21
Advanced Topics in Image Classification
Advanced topics, including attention mechanisms and generative models for image classification
Module #22
Explainability and Interpretability
Techniques for explaining and interpreting deep learning models for image classification
Module #23
Deep Learning for Multi-Label Classification
Techniques for handling multi-label classification problems using deep learning
Module #24
Deep Learning for Image Classification with Limited Data
Techniques for image classification with limited data, including few-shot learning and transfer learning
Module #25
GPU Optimization and Parallelization
Techniques for optimizing and parallelizing deep learning models for image classification using GPUs
Module #26
Cloud-Based Deep Learning
Using cloud-based services for deep learning, including AWS, Google Colab, and Microsoft Azure
Module #27
Ethical Considerations in Image Classification
Ethical considerations and biases in image classification, including fairness and transparency
Module #28
Deploying Deep Learning Models
Techniques for deploying deep learning models for image classification, including model serving and API development
Module #29
Project Presentations and Feedback
Students present their projects and receive feedback from instructors and peers
Module #30
Course Wrap-Up & Conclusion
Planning next steps in Deep Learning for Image Classification career


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