Convo Nets for Visual Recognition: Computer Vision & CNN Architectures
Neural Networks
| Intermediate
- 10 Videos | 48m 56s
- Includes Assessment
- Earns a Badge
Learners can explore the machine learning concept and classification of activation functions, the limitations of Tanh and the limitations of Sigmoid, and how these limitations can be resolved using the rectified linear unit, or ReLU, along with the significant benefits afforded by ReLU, in this 10-video course. You will observe how to implement ReLU activation function in convolutional networks using Python. Next, discover the core tasks used in implementing computer vision, and developing CNN models from scratch for object image classification by using Python and Keras. Examine the concept of the fully-connected layer and its role in convolutional networks, and also the CNN training process workflow and essential elements that you need to specify during the CNN training process. The final tutorial in this course involves listing and comparing the various convolutional neural network architectures. In the concluding exercise you will recall the benefits of applying ReLU in CNNs, list the prominent CNN architectures, and implement ReLU function in convolutional networks using Python.
WHAT YOU WILL LEARN
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discover the key concepts covered in this coursedefine and classify activation functions and provide a comparative analysis with the pros and cons of the different types of activation functionsrecognize the limitations of Sigmoid and Tanh and describe how they can be resolved using ReLU along with the significant benefits afforded by ReLU when applied in convolutional networksimplement rectified linear activation function in convolutional networks using Pythonlist the core tasks that are used in the implementation of computer vision
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develop convolutional neural network models from the scratch for object photo classification using Python and Kerasdescribe the concept of fully-connected layer and illustrate its role in convolutional networksdescribe the convolutional neural network training process workflow and the essential elements that we need to specify during the training processlist and compare the various architectures of convolutional neural networksrecall the benefits of applying ReLU in convolutional neural networks, list the prominent architectures of convolutional neural networks and implement ReLU function in convolutional networks using Python
IN THIS COURSE
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1.Course Overview1m 3sUP NEXT
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2.Activation Functions and Types10m 48s
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3.Why ReLU in Convolutional Neural Networks4m 20s
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4.Implementing ReLU3m 58s
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5.Computer Vision Tasks4m 58s
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6.Developing Object Photo Classification Model6m 31s
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7.Fully-connected Layer3m 33s
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8.Convolutional Neural Network Training Process3m 55s
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9.Convolutional Neural Network Architectures6m 2s
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10.Exercise: Applying ReLU in CNN3m 49s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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