Deep Learning CNN with Python Online Course

Deep Learning CNN with Python Online Course

Convolutional Neural Networks (CNNs) revolutionized computer vision after AlexNet in 2012 and have since become indispensable in areas ranging from image recognition and object detection to audio processing and reinforcement learning. Mastering CNNs is now essential for anyone pursuing a career in data science.

This course begins by introducing the importance of CNNs in data science and revisiting classical computer vision techniques such as image processing and object detection. You will then build a strong foundation in deep neural networks with topics like perceptrons and multi-layered perceptrons before diving deep into CNNs. Key topics include:

• CNN architecture and gradient descent in CNNs
• Using TensorFlow for CNN implementation
• Classical CNN architectures and transfer learning
• A hands-on YOLO case study for object detection

Who should take this Course? 

The Deep Learning CNN with Python Online Course is ideal for data scientists, machine learning engineers, AI enthusiasts, and developers who want to master convolutional neural networks for image recognition and computer vision tasks. It is also suitable for students, researchers, and professionals in areas like healthcare, robotics, and automation who are eager to apply CNNs using Python to solve real-world challenges with deep learning.

What you will learn

• Understand the importance of CNNs in data science
• Explore the reasons to shift from classical computer vision to CNNs
• Learn concepts from the beginning with comprehensive unfolding with examples in Python
• Study the evolutions of CNNs from LeNet (1990s) to MobileNets (2020s)
• Deep-dive into CNNs with examples of training CNNs from scratch
• Build your own applications for human face verification and neural style transfer

Course Outline

Introduction to the Course

• Course Overview
• Introduction to Instructor
• Why CNN
• Focus of the Course

Image Processing

• Gray-Scale Images
• Gray-Scale Images Quiz
• Gray-Scale Images Solution
• RGB Images
• RGB Images Quiz
• RGB Images Solution
• Reading and Showing Images in Python
• Reading and Showing Images in Python Quiz
• Reading and Showing Images in Python Solution
• Converting an Image to Grayscale in Python
• Converting an Image to Grayscale in Python Quiz
• Converting an Image to Grayscale in Python Solution
• Image Formation
• Image Formation Quiz
• Image Formation Solution
• Image Blurring 1
• Image Blurring 1 Quiz
• Image Blurring 1 Solution
• Image Blurring 2
• Image Blurring 2 Quiz
• Image Blurring 2 Solution
• General Image Filtering
• Convolution
• Edge Detection
• Image Sharpening
• Implementation of Image Blurring Edge Detection Image Sharpening in Python
• Parametric Shape Detection
• Image Processing
• Image Processing Activity
• Image Processing Activity Solution

Object Detection

• Introduction to Object Detection
• Classification Pipeline
• Classification Pipeline Quiz
• Classification Pipeline Solution
• Sliding Window Implementation
• Shift Scale Rotation Invariance
• Shift Scale Rotation Invariance Exercise
• Person Detection
• HOG Features
• HOG Features Exercise
• Hand Engineering Versus CNNs
• Object Detection Activity

Deep Neural Network Overview

• Neuron and Perceptron
• DNN Architecture
• DNN Architecture Quiz
• DNN Architecture Solution
• FeedForward FullyConnected MLP
• Calculating Number of Weights of DNN
• Calculating Number of Weights of DNN Quiz
• Calculating Number of Weights of DNN Solution
• Number of Neurons Versus Number of Layers
• Discriminative Versus Generative Learning
• Universal Approximation Theorem
• Why Depth
• Decision Boundary in DNN
• Decision Boundary in DNN Quiz
• Decision Boundary in DNN Solution
• BiasTerm
• BiasTerm Quiz
• BiasTerm Solution
• Activation Function
• Activation Function Quiz
• Activation Function Solution
• DNN Training Parameters
• DNN Training Parameters Quiz
• DNN Training Parameters Solution
• Gradient Descent
• Backpropagation
• Training DNN Animation
• Weight Initialization
• Weight Initialization Quiz
• Weight Initialization Solution
• Batch MiniBatch Stochastic Gradient Descent
• Batch Normalization
• Rprop and Momentum
• Rprop and Momentum Quiz
• Rprop and Momentum Solution
• Convergence Animation
• DropOut, Early Stopping and Hyperparameters
• DropOut, Early Stopping and Hyperparameters Quiz
• DropOut, Early Stopping and Hyperparameters Solution

Deep Neural Network Architecture

• Convolution Revisited
• Implementing Convolution in Python Revisited
• Why Convolution
• Filters Padding Strides
• Padding Image
• Pooling Tensors
• CNN Example
• Convolution and Pooling Details
• MaxPooling Exercise
• NonVectorized Implementations of Conv2d and Pool2d
• Deep Neural Network Architecture Activity

Gradient Descent in CNNs

• Example Setup
• Why Derivatives
• Why Derivatives Quiz
• Why Derivatives Solution
• What Is Chain Rule
• Applying Chain Rule
• Gradients of MaxPooling Layer
• Gradients of MaxPooling Layer Quiz
• Gradients of MaxPooling Layer Solution
• Gradients of Convolutional Layer
• Extending to Multiple Filters
• Extending to Multiple Layers
• Extending to Multiple Layers Quiz
• Extending to Multiple Layers Solution
• Implementation in NumPy ForwardPass
• Implementation in NumPy BackwardPass 1
• Implementation in NumPy BackwardPass 2
• Implementation in NumPy BackwardPass 3
• Implementation in NumPy BackwardPass 4
• Implementation in NumPy BackwardPass 5
• Gradient Descent in CNNs Activity

Introduction to TensorFlow

• Introduction
• FashionMNIST Example Plan Neural Network
• FashionMNIST Example CNN
• Introduction to TensorFlow Activity

Classical CNNs

• LeNet
• LeNet Quiz
• LeNet Solution
• AlexNet
• VGG
• InceptionNet
• GoogLeNet
• Resnet
• Classical CNNs Activity

Transfer Learning

• What is Transfer learning
• Why Transfer Learning
• ImageNet Challenge
• Practical Tips
• Project in TensorFlow
• Transfer Learning Activity

YOLO

• Image Classification Revisited
• Sliding Window Object Localization
• Sliding Window Efficient Implementation
• YOLO Introduction
• YOLO Training Data Generation
• YOLO Anchor Boxes
• YOLO Algorithm
• YOLO Non-Maxima Suppression
• RCNN
• YOLO Activity

Face Verification

• Problem Setup
• Project Implementation
• Face Verification Activity

Neural Style Transfer

• Problem Setup
• Implementation TensorFlow Hub
• Thank You and Conclusion