Convolutional Neural Network
Convolutional Neural Network
Duration: 56m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 286 MB
Genre: eLearning | Language: English
Duration: 56m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 286 MB
Genre: eLearning | Language: English
Learn the fundamental aspects to design a convolutional neural network architecture by providing steps of modeling and c
What you'll learn
An overview of the deep learning field
Details to design a Convolutional Neural Network architecture
Limitation, future and challenges of convolutional neural networks
Requirements
Basic math (Convolution, matrix, probability)
Basic knowledge of image processing
Description
The artificial intelligence is a large field includes many techniques to make machine thinks. which means endowed this machine by intelligence, unlike, as all known, the habitual intelligence are exhibited by humans and animals. Therefore, in this course, we investigate the mimicking of human intelligence on machines by introducing a modern algorithm of artificial intelligence named convolutional neural network which is a technique of deep learning for computers to make the machine learn and expert. In this course, we present an overview of deep learning in which, we introduce the notion and classification of convolutional neural networks. We gives also the definition and the advantages of CNNs. In this course, we provide the tricks to elaborate your own architecture of CNN and the hardware and software to design a CNN model. In the end, we present the limitation and future challenges of CNN.
The essential points tackled in this course are given as follow:
- What is deep learning?
- Why computational Intelligence algorithms are used?
- Biomimetics inspiration of CNN from brain
- Classification of deep learning (CNN)
- The kinds of deep learning algorithms
- Definition of convolutional neural networks
- Advantages of Convolutional Neural Network
- The aim of CNN
- Architecture of CNN
- Training and optimization of CNN parameters
- Hardware material used for CNN
- Software used for deep learning
- Famous CNN architecture
- Application of CNN
- Limitation of CNN
- Future and challenges of convolutional neural networks
- Conclusions
Who this course is for:
Engineering Academics
Passion and Interest for learning concepts of artificial intelligence
University students of Computer Science
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