Deep Learning CNN with Python Practice Exam
Deep Learning CNN with Python Practice Exam
About the Deep Learning CNN with Python Exam
Deep Learning with Convolutional Neural Networks (CNNs) using Python focuses on building and training CNN models for image recognition and computer vision tasks. Using Python and libraries like TensorFlow and Keras, learners will explore CNN architectures, including convolutional layers, pooling, and fully connected layers. The course covers techniques for feature extraction, image classification, object detection, and more.
Skills Required to learn
- Proficiency in Python programming
- Understanding of linear algebra, including matrices and vectors
- Basic knowledge of machine learning concepts and algorithms
- Familiarity with neural networks and their components (layers, activation functions, etc.)
- Experience in handling and preprocessing image data
- Proficiency with libraries like NumPy, Pandas, Matplotlib, and TensorFlow/Keras
- Understanding of convolutional layers, pooling layers, and fully connected layers in CNNs
Knowledge Gained
- Ability to build and train Convolutional Neural Networks (CNNs) using Python and TensorFlow/Keras
- Understanding of CNN architectures and their components, including convolutional layers, pooling, and fully connected layers
- Skills in applying CNNs to image classification, object detection, and other computer vision tasks
- Experience with techniques like data augmentation, feature extraction, and transfer learning for CNNs
- Knowledge of model optimization and tuning for better performance
- Hands-on experience in building efficient deep learning models for real-world applications like healthcare, autonomous vehicles, and entertainment
- Understanding of how to preprocess and prepare image data for CNN models.
Who should take the Exam?
- Aspiring machine learning engineers and data scientists interested in specializing in computer vision
- AI professionals looking to deepen their knowledge of deep learning and CNNs
- Software developers aiming to enhance their skills in image recognition and computer vision tasks
- Students and professionals seeking to demonstrate expertise in building CNN models with Python
- Individuals interested in applying CNNs to real-world problems in industries like healthcare, automotive, and entertainment
- Those preparing for roles in machine learning, deep learning, and AI development, with a focus on visual data analysis
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 to TensorFlow
- 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
