Master Computer Vision with Deep Learning: From Images to Insights

What you will learn:

Master the foundations of computer vision using traditional and deep learning methods.
Develop a deep understanding of Convolutional Neural Networks (CNNs) and their applications.
Build practical projects in image classification, object detection, and semantic segmentation using ConvNets.
Effectively utilize transfer learning to solve real-world computer vision problems.
Visualize and debug ConvNets to understand their internal dynamics.
Apply data augmentation and effectively handle large and small datasets.
Work with time-series and video data using Spatio-temporal models.
Understand and apply 3D deep learning techniques to analyze 3D datasets.

Description

Unlock the power of deep learning for computer vision! This comprehensive course takes you from fundamental image processing to advanced techniques like semantic segmentation and 3D deep learning. We'll start with the basics of traditional computer vision, using OpenCV and Pillow to perform tasks like thresholding, denoising, and edge detection, culminating in building real-world applications like car license plate detection. Then, we dive deep into convolutional neural networks (CNNs), covering architectures like VGG, Inception, and DenseNet. You'll learn how to build and optimize CNNs for various tasks, even with limited data, using techniques such as transfer learning and data augmentation. We'll explore practical applications like semantic segmentation using U-Net, object detection with SSD and YOLO, and the analysis of video data with spatio-temporal models. Finally, we'll extend your skills into the exciting world of 3D deep learning, working with 3D data like LiDAR point clouds. Gain hands-on experience, master debugging techniques, and build a strong portfolio of projects. This course is perfect for aspiring computer vision professionals, data scientists, and anyone passionate about leveraging AI to unlock the potential of images and videos.

Curriculum

Introduction

This introductory section sets the stage for the course, providing a foundational overview of the topics covered and what you can expect to achieve by the end of the course. The single lecture covers the overall course structure and learning objectives.

Part 1: From Traditional CV to Deep Learning

This section bridges the gap between traditional computer vision and the deep learning revolution. We'll explore core CV tasks and then transition to the power of learnable parameters in CNNs, setting the groundwork for our deep dive into neural network architectures. The section features an introduction to traditional computer vision, covering image processing fundamentals using OpenCV and Pillow. We will also build a practical Car License Plate Detection (LPD) system, visualizing the traditional computer vision pipeline. Finally, the evolution from traditional filters to learnable convolution filters using Deep Learning is introduced.

Module 1.1: From traditional ConvNets to DL

This module provides a comprehensive introduction to traditional computer vision pipelines and the transition to deep learning. Lectures cover image pre-processing techniques, a practical example of License Plate Detection, and a detailed explanation of the shift from traditional filters to learnable parameters in convolutional neural networks (ConvNets).

Module 1.2: ConvNets

This module dives deep into the fundamentals of Convolutional Neural Networks (ConvNets). We will explore concepts like convolution, filtering, feature maps, and size calculations. You'll build a Vanilla ConvNet for image classification, learn about hyperparameters, and understand how to optimize ConvNet efficiency. This practical module lays the crucial groundwork for understanding more complex architectures.

Module 1.3: ConvNet Meta Architectures

Building upon the fundamentals, this module introduces advanced ConvNet architectures. We’ll explore design patterns like encoder, encoder-decoder, VGG, Inception, skip connections, and DenseNets, understanding their strengths and applications in various computer vision tasks. This module equips you with a thorough understanding of state-of-the-art architectures.

Module 2.1: ConvNets with small datasets

This module addresses the challenges of working with limited datasets in computer vision. We'll explore techniques like data augmentation, the creation of image data generators, and regularization methods such as dropout. You will learn practical ETL pipeline implementation, using Python generators for efficient data handling. A Cats vs. Dogs example will illustrate the concepts in practice.

Module 2.2: Transfer Learning

Transfer learning is a powerful technique that enables leveraging pre-trained models to improve performance, especially with small datasets. This module covers the principles of transfer learning, exploring scenarios such as using a ConvNet as a feature extractor and fine-tuning a pre-trained convolutional base. You will learn how and when to apply transfer learning effectively.

Module 2.3: ConvNet visualization and debugging

This module focuses on essential debugging and visualization techniques. You'll learn how to visualize intermediate feature maps, kernels, and use Class Activation Maps (GradCAM) to gain insights into your ConvNets' decision-making process, enabling effective model improvement and troubleshooting.

Module 3.1: Semantic Segmentation

This module introduces semantic segmentation, a crucial task in computer vision. You'll build a U-Net architecture from scratch, using the CAMVID dataset as a practical example. This section will cover encoder and decoder design, downsampling and upsampling techniques, culminating in a fully functional semantic segmentation model.

Module 3.2: Object Detection

Object detection forms a significant part of modern computer vision. This module covers the taxonomy of object detection methods, comparing traditional approaches with deep learning models. We will discuss two-stage object detectors and single-shot detectors (SSD and YOLO), providing a comprehensive understanding of this important area.

Module 3.3: Spatio-Temporal models

This module tackles the complexities of analyzing video data. We'll cover spatio-temporal deep learning models, exploring frame stacking, Conv1D models, and recurrent models like CNN-LSTM and ConvLSTM, allowing you to process and extract information from video sequences effectively.

Module 3.4: 3D Deep Learning

We will extend the applications of ConvNets to 3D data. This module introduces the basics of 3D deep learning, demonstrating how to handle 3D datasets such as LiDAR point clouds and exploring relevant 3D deep learning methods. This will equip you to tackle increasingly complex and realistic computer vision scenarios.

Resources

This final section provides access to valuable supplementary resources to further enhance your learning experience. This includes notebooks and slides for reference and additional practice.

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