Convo Nets for Visual Recognition: Computer Vision & CNN Architectures

Neural Networks
  • 10 Videos | 52m 56s
  • Includes Assessment
  • Earns a Badge
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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

  • discover the key concepts covered in this course
    define and classify activation functions and provide a comparative analysis with the pros and cons of the different types of activation functions
    recognize 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 networks
    implement rectified linear activation function in convolutional networks using Python
    list the core tasks that are used in the implementation of computer vision
  • develop convolutional neural network models from the scratch for object photo classification using Python and Keras
    describe the concept of fully-connected layer and illustrate its role in convolutional networks
    describe the convolutional neural network training process workflow and the essential elements that we need to specify during the training process
    list and compare the various architectures of convolutional neural networks
    recall 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

  • Playable
    1. 
    Course Overview
    1m 3s
    UP NEXT
  • Playable
    2. 
    Activation Functions and Types
    10m 48s
  • Locked
    3. 
    Why ReLU in Convolutional Neural Networks
    4m 20s
    FREE ACCESS
  • Locked
    4. 
    Implementing ReLU
    3m 58s
    FREE ACCESS
  • Locked
    5. 
    Computer Vision Tasks
    4m 58s
    FREE ACCESS
  • Locked
    6. 
    Developing Object Photo Classification Model
    6m 31s
    FREE ACCESS
  • Locked
    7. 
    Fully-connected Layer
    3m 33s
    FREE ACCESS
  • Locked
    8. 
    Convolutional Neural Network Training Process
    3m 55s
    FREE ACCESS
  • Locked
    9. 
    Convolutional Neural Network Architectures
    6m 2s
    FREE ACCESS
  • Locked
    10. 
    Exercise: Applying ReLU in CNN
    3m 49s
    FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.

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