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Convolutional Neural Networks | CNN

Course Reviews

Track :

Programming

Lessons no : 42

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What will you learn in this course?
  • Master how to design and implement convolutional neural networks for image recognition and classification tasks using deep learning frameworks
  • Apply CNN architectures such as LeNet, AlexNet, and ResNet to real-world image processing problems effectively
  • Optimize CNN models through techniques like dropout, batch normalization, and data augmentation for improved accuracy and robustness
  • Utilize transfer learning with pre-trained CNN models to accelerate development and enhance performance in image analysis projects
  • Analyze and troubleshoot CNN models to identify issues related to overfitting, underfitting, and vanishing gradients
  • Implement convolutional, pooling, and fully connected layers to build efficient neural network architectures for visual data
  • Evaluate CNN model performance using metrics like accuracy, precision, recall, and F1 score in image recognition tasks
  • Integrate CNNs with other machine learning techniques to develop comprehensive solutions for complex image processing challenges
  • Apply best practices for preprocessing pixel data, including normalization and augmentation, to improve CNN training outcomes
  • Develop skills to deploy CNN models in real-time applications such as object detection, facial recognition, and autonomous vehicles
  • Understand the theoretical foundations of convolutional operations, feature extraction, and hierarchical learning in CNNs
  • Leverage deep learning libraries like TensorFlow and PyTorch to build, train, and fine-tune convolutional neural networks

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Lessons | 42
CNN 1 Computer Vision | Convolutional Neural Networks
00:14:56

CNN 1 Computer Vision | Convolutional Neural Networks
CNN 2 Edge Detection | Convolutional Neural Networks
00:15:55

CNN 2 Edge Detection | Convolutional Neural Networks
CNN 3 Padding | Padded Convolution | Convolutional Neural Networks
00:10:05

CNN 3 Padding | Padded Convolution | Convolutional Neural Networks
CNN 4 Strides | Strided Convolution | Convolutional Neural Networks
00:05:46

CNN 4 Strides | Strided Convolution | Convolutional Neural Networks
CNN 5 Convolution over Volume | Convolutional Neural Networks
00:07:54

CNN 5 Convolution over Volume | Convolutional Neural Networks
CNN W1L07 One layer of Convolutional Network
00:16:11

CNN W1L07 One layer of Convolutional Network
CNN W1L08 Simple Convolutional Network Example
00:08:32

CNN W1L08 Simple Convolutional Network Example
CNN W1L09 Pooling Layers
00:10:26

CNN W1L09 Pooling Layers
CNN W1L10 CNN Example
00:12:37

CNN W1L10 CNN Example
CNN W1L11 Why Convolutions
00:09:41

CNN W1L11 Why Convolutions
CNN W2L01 Why look at case studies
00:03:08

CNN W2L01 Why look at case studies
CNN W2L02 Classic Networks
00:18:19

CNN W2L02 Classic Networks
CNN W2L03 ResNets
00:07:08

CNN W2L03 ResNets
CNN W2L04 Why ResNets Work
00:09:13

CNN W2L04 Why ResNets Work
CNN W2L05 Networks in Networks and 1x1 Convolutions
00:06:41

CNN W2L05 Networks in Networks and 1x1 Convolutions
CNN W2L06 Inception Network Motivation
00:10:15

CNN W2L06 Inception Network Motivation
CNN W2L07 Inception Network
00:08:47

CNN W2L07 Inception Network
CNN W2L08 Using Open source Implementation
00:04:57

CNN W2L08 Using Open source Implementation
CNN W2L09 Transfer Learning
00:08:48

CNN W2L09 Transfer Learning
CNN W2L10 Data Augmentation
00:09:32

CNN W2L10 Data Augmentation
CNN W2L11 State of Computer Vision
00:12:39

CNN W2L11 State of Computer Vision
CNN W3L01 Object Localization
00:11:55

CNN W3L01 Object Localization
CNN W3L02 Landmark Detection
00:05:57

CNN W3L02 Landmark Detection
CNN W3L03 Object Detection
00:05:49

CNN W3L03 Object Detection
CNN W3L04 Convolutional Implementation of Sliding Windows
00:11:09

CNN W3L04 Convolutional Implementation of Sliding Windows
CNN W3L05 Bounding Box Predictions
00:14:32

CNN W3L05 Bounding Box Predictions
CNN W3L06 Intersection Over Union
00:04:19

CNN W3L06 Intersection Over Union
CNN W3L07 Non max Suppression
00:08:03

CNN W3L07 Non max Suppression
CNN W3L08 Anchor Boxes
00:09:43

CNN W3L08 Anchor Boxes
CNN W3L09 YOLO Algorithm
00:07:02

CNN W3L09 YOLO Algorithm
CNN W3L10 Region Proposals Optional
00:06:28

CNN W3L10 Region Proposals Optional
CNN W4L01 What is Face Recognition
00:04:38

CNN W4L01 What is Face Recognition
CNN W4L02 One Shot Learning
00:04:45

CNN W4L02 One Shot Learning
CNN W4L03 Siamese Network
00:04:52

CNN W4L03 Siamese Network
CNN W4L04 Triplet Loss
00:15:31

CNN W4L04 Triplet Loss
CNN W4L05 Face Verification and Binary Classification
00:06:06

CNN W4L05 Face Verification and Binary Classification
CNN W4L06 What is Neural Style Transfer
00:02:03

CNN W4L06 What is Neural Style Transfer
CNN W4L07 What are Deep ConvNets Learning
00:07:58

CNN W4L07 What are Deep ConvNets Learning
CNN W4L08 Neural Style Transfer Cost Function
00:04:00

CNN W4L08 Neural Style Transfer Cost Function
CNN W4L09 Content Cost Function
00:03:38

CNN W4L09 Content Cost Function
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A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.