Deep Learning CNN: Convolutional Neural Networks with Python [Video]
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-
Free ChapterIntroduction to 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
-
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
-
Classical CNNs
-
Transfer Learning
-
YOLO
-
Face Verification
-
Neural Style Transfer
About this
video
Convolutional Neural Networks (CNNs) are considered game-changers in the field of computer vision, particularly after AlexNet in 2012. They are everywhere now, ranging from audio processing to more advanced reinforcement learning. So, the understanding of CNNs becomes almost inevitable in all fields of data science. With this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in data science.
The course starts with introducing and jotting down the importance of Convolutional Neural Networks (CNNs) in data science. You will then look at some classical computer vision techniques such as image processing and object detection. It will be followed by deep neural networks with topics such as perceptron and multi-layered perceptron. Then, you will move ahead with learning in-depth about CNNs. You will first look at the architecture of a CNN, then gradient descent in CNN, get introduced to TensorFlow, classical CNNs, transfer learning, and a case study with YOLO.
Finally, you will work on two projects: Neural Style Transfer (using TensorFlow-hub) and Face Verification (using VGGFace2).
By the end of this course, you will have understood the methodology of CNNs with data science using real datasets. Apart from this, you will easily be able to relate the concepts and theories in computer vision with CNNs.
All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Deep-Learning-CNN-Convolutional-Neural-Networks-with-Python
- Publication date:
- August 2022
- Publisher
- Packt
- Duration
- 15 hours 26 minutes
- ISBN
- 9781803243726