Course Description
This chapter will introduce neural network design concepts. It will cover the importance of data in both the performance and selection of neural networks. It will also cover what activation functions, loss functions, and optimizers are as well as how to select which ones to use. This chapter will also introduce you to training, testing, and validation for neural networks.
What You'll Learn
- Understand the importance of data in neural network performance and selection
- Identify different types of neural networks such as MLP and Convolutional networks
- Determine the appropriate neural network type based on data type
- Select activation functions, loss functions, and optimizers for a neural network
- Configure neural network components including hyperparameters and input, output, and hidden layers
- Apply training, testing, and validation to neural networks
Key Takeaways
- Data plays a central role in both the performance and the selection of neural networks.
- Choosing a neural network involves matching the network type to the type of data being used.
- Activation functions, loss functions, and optimizers are core components, and the course explains how to select which ones to use.
- Neural networks rely on training, testing, and validation as part of their design process.
- Designing a neural network includes setting hyperparameters and configuring input, output, and hidden layers.
Frequently Asked Questions
What does this course cover?
It introduces neural network design concepts, including the importance of data in neural network performance and selection, what activation functions, loss functions, and optimizers are and how to select them, and training, testing, and validation for neural networks.
What skills will I gain from this course?
The course builds skills in Algorithm Design, Artificial Neural Networks, Deep Learning, Deep Learning Methods, Loss Functions, and Neural Engineering.
What topics are included in the lessons?
Lessons cover the Importance of Data; Types of Neural Networks (MLP, Convolutional, etc.); Determining Neural Network Type based on Data Type; Activation Functions, Loss Functions, and Optimizers; Hyperparameters; Layers (Input, Output, Hidden); and Training, Testing, Validation.
Will I learn how to choose the right neural network?
Yes. The course covers how to select a neural network, including determining the neural network type based on the data type.
