Course details

ConvNets: Working with Convolutional Neural Networks

ConvNets: Working with Convolutional Neural Networks


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Learners can explore the prominent machine learning elements that are used for computation in artificial neural networks, the concept of edge detection, and common algorithms, as well as convolution and pooling operations, and essential rules of filters and channel detection, in this 10-video course. Key concepts covered here include the architecture of neural networks, along with essential elements used for computations by focusing on Softmax classifier; how to work with ConvNetJS as a Javascript library and train deep learning models; and learning about the edge detection method, including common algorithms that are used for edge detection. Next, you will examine the series of convolution and pooling operations used to detect features; learn the involvement of math in convolutional neural networks and essential rules that are applied on filters and channel detection; and learn principles of convolutional layer, activation function, pooling layer, and fully-connected layer. Learners will observe the need for activation layers in convolutional neural networks and compare prominent activation functions for deep neural networks; and learn different approaches to improve convolution neural networks and machine learning systems.



Expected Duration (hours)
0.7

Lesson Objectives

ConvNets: Working with Convolutional Neural Networks

  • discover the key concepts covered in this course
  • recall the architecture of neural networks along with the essential elements used for computations with focus on softmax classifier
  • work with ConvNetJS as a Javascript library and train deep learning models
  • define the concept of the edge detection method and list the common algorithms that are used for edge detection
  • recognize the series of convolution and pooling operations to detect features
  • recognize the involvement of maths in convolutional neural networks and recall the essential rules that are applied on filters and channel detection
  • illustrate the principle of convolutional layer, activation function, pooling layer and fully-connected layer
  • recognize the need for activation layer in convolutional neural networks and compare the prominent activation functions for deep neural networks
  • recall the different approaches to improve convolution neural networks and machine learning systems
  • list the common algorithms that are used for edge detection, recall the essential rules that are applied on filters and channel detection and specify some of the critical approaches that we can adopt to improve convolutional neural networks
  • Course Number:
    it_mlfscndj_02_enus

    Expertise Level
    Intermediate