Introduction To Deep Learning

This section introduces Deep Learning, focusing specifically on Convolutional Neural Networks (CNNs) and how they revolutionize image processing by overcoming limitations of traditional neural networks.

Module Objectives (For Week 12)

This section outlines the objectives for Week 12, focusing on Convolutional Neural Networks (CNNs).

Week 12: Convolutional Neural Networks (Cnns)

This section introduces Convolutional Neural Networks (CNNs), highlighting their importance in image processing and computer vision by overcoming limitations of traditional ANNs.

Motivation For Cnns In Image Processing: Overcoming Ann Limitations

This section explores the limitations of traditional Artificial Neural Networks (ANNs) when processing images and introduces Convolutional Neural Networks (CNNs) as a powerful solution.

The Problem With Fully Connected Anns For Images

Fully connected Artificial Neural Networks (ANNs) face significant challenges when processing image data due to high dimensionality, an explosion of parameters, and the loss of vital spatial information.

The Cnn Solution

This section explains Convolutional Neural Networks (CNNs) and their advantages over traditional Artificial Neural Networks (ANNs) for image processing tasks.

Convolutional Layers: The Feature Extractors

Convolutional layers are integral to Convolutional Neural Networks (CNNs), designed to automatically learn and extract features from input images using filters and the convolution operation.

The Core Idea: Filters (Kernels) And Convolution Operation

This section delves into the foundational mechanisms of convolutional layers in CNNs, highlighting the roles of filters and the convolution operation in feature extraction from images.

Feature Maps (Activation Maps): The Output Of Convolution

Feature maps are 2D arrays generated by filters in convolutional layers, representing the strength of detected patterns in an input image.

Pooling Layers: Downsampling And Invariance

Pooling layers in Convolutional Neural Networks (CNNs) help reduce spatial dimensions and enhance invariance to small shifts in input data.

The Core Idea: Downsampling

Downsampling in Convolutional Neural Networks (CNNs) reduces the spatial dimensions of feature maps while retaining essential information.

Types Of Pooling

This section explains the purpose and types of pooling layers in Convolutional Neural Networks (CNNs), particularly Max Pooling and Average Pooling, highlighting their benefits for reducing dimensionality and improving translation invariance.

Basic Cnn Architectures: Stacking The Layers

This section discusses the fundamental architecture of Convolutional Neural Networks (CNNs), highlighting the flow of data through various layers to extract increasingly complex features from images.

General Flow

This section provides an overview of the general flow of Convolutional Neural Networks (CNNs), emphasizing their architecture and operational principles.

Example Architecture (Conceptual)

This section provides an overview of the architecture of Convolutional Neural Networks (CNNs), focusing on the flow from input images through convolutional and pooling layers to the output classification.

Regularization For Deep Learning: Preventing Overfitting

Regularization techniques like Dropout and Batch Normalization are essential for improving the generalization of deep learning models by mitigating overfitting.

Dropout

Dropout is a regularization technique designed to prevent overfitting in neural networks by randomly deactivating a portion of neurons during training.

Batch Normalization

Batch Normalization is a technique used in deep learning to normalize the outputs of a layer for each mini-batch, speeding up training and improving model performance.

Transfer Learning: Leveraging Pre-Trained Models (Conceptual)

Transfer learning allows leveraging pre-trained models to overcome challenges in training deep models from scratch, thus saving time and resources.

The Core Idea

This section introduces the significance of Convolutional Neural Networks (CNNs) in deep learning, highlighting their innovative architecture designed to facilitate image processing tasks.

Common Transfer Learning Strategies (Conceptual)

This section introduces Transfer Learning strategies in neural networks, specifically focusing on feature extraction and fine-tuning pre-trained models.

Lab: Building And Training A Basic Cnn For Image Classification Using Keras

This lab focuses on hands-on experience in constructing and training a Convolutional Neural Network for image classification using the Keras API.

Lab Objectives

This section outlines the objectives for the lab exercise focusing on building and training Convolutional Neural Networks (CNNs) using Keras.

Activities

This section describes the hands-on activities for learning about Convolutional Neural Networks (CNNs) within deep learning.

Dataset Preparation

This section focuses on the critical steps involved in preparing datasets for training Convolutional Neural Networks (CNNs), emphasizing the importance of proper data handling.

Load Dataset

This section provides the foundational steps needed to load an image dataset for training a Convolutional Neural Network (CNN) using the Keras API.

Data Reshaping (For Cnns)

Data reshaping is crucial for ensuring the proper format of image data when inputting it into Convolutional Neural Networks (CNNs), allowing for effective training and performance.

Normalization

Normalization is a crucial process in deep learning that helps stabilize the training of Convolutional Neural Networks (CNNs) by standardizing inputs.

One-Hot Encode Labels

One-hot encoding is a method of converting categorical integer labels into a binary format usable by machine learning algorithms.

Train-Test Split

The Train-Test Split is a crucial method in machine learning used to evaluate model performance by separating the dataset into training and testing subsets.

Building A Basic Cnn Architecture Using Keras

This section focuses on the practical implementation of a basic Convolutional Neural Network (CNN) architecture using the Keras library, emphasizing hands-on learning through a lab exercise.

Import Keras Components

This section introduces the essential Keras components for building a basic Convolutional Neural Network (CNN) architecture.

Sequential Model

The Sequential Model is a foundational structure in deep learning that organizes layers linearly, facilitating the construction of neural networks.

First Convolutional Block

This section introduces the first convolutional block in a Convolutional Neural Network (CNN), detailing the convolution operation and the significance of convolutional and pooling layers in automatically extracting image features.

Second Convolutional Block (Optional But Recommended)

The second convolutional block refers to the optional layer structure in a Convolutional Neural Network (CNN) which enhances the model's ability to learn hierarchical features from images.

Flatten Layer

The Flatten Layer is essential in Convolutional Neural Networks (CNNs) as it converts three-dimensional feature maps into one-dimensional vectors to prepare data for fully connected layers.

Dense (Fully Connected) Hidden Layer

This section discusses the configuration and significance of dense (fully connected) hidden layers in neural networks, focusing on how they process high-level features derived from preceding layers.

Output Layer

This section explores the role of the output layer in a Convolutional Neural Network (CNN), detailing its architecture and functional significance in classification tasks.

Model Summary

This section provides a comprehensive overview of Convolutional Neural Networks (CNNs) in deep learning, focusing on their architecture, advantages over traditional ANNs, and key concepts such as convolutional layers and pooling techniques.

Compiling The Cnn

This section explores the architecture and key components of Convolutional Neural Networks (CNNs), explaining their advantages over traditional Artificial Neural Networks (ANNs) in image processing.

Training The Cnn

This section focuses on the fundamental concepts and architecture of Convolutional Neural Networks (CNNs), emphasizing their advantages in image processing over traditional ANNs.

Evaluating The Cnn

This section discusses the evaluation of Convolutional Neural Networks (CNNs), focusing on their architecture, the roles of layers, and best practices for building and assessing a CNN’s performance.

Conceptual Exploration Of Hyperparameters

This section explores hyperparameters in Convolutional Neural Networks (CNNs) and their crucial roles in architecture design and model performance.

Self-Reflection Questions For Students

This section presents self-reflection questions designed to deepen students' understanding of Convolutional Neural Networks (CNNs) and their architecture.