Enjoying the preview?
This is the free first lesson. Get full access — request a demo or sign in.
Neural Networks
In this course, you’ll learn the basics of neural networks. You’ll receive an introduction to neural networks and closely related concepts and topics. You’ll…Preview the first lesson free — get full access to all 24 lessons.
Beginner Provider : Gabriel Popoola 6 chapters · 24 Lessons · 1h 25m in Arabic, German, English, Spanish, French, Portuguese, Urdu, Chinese
Course Description
In this course, you’ll learn the basics of neural networks. You’ll receive an introduction to neural networks and closely related concepts and topics. You’ll be able to understand the various design aspects of neural networks and how to go about making selections for them. The course will also cover how to implement neural networks—specifically in python. The advantages of different neural network frameworks and implementations will be discussed. All of the concepts and theories will be tied together through examples as well as the discussion of practical applications. By the end of this course, you will be aware of multiple ways to implement a neural network and how to go about selecting algorithms, methods, and values for the various aspects of a neural network.
What You'll Learn
- Understand what neural networks are and how they are used
- Identify the various design aspects of neural networks and how to make selections for them
- Distinguish different types of neural networks, including MLP and convolutional networks
- Implement neural networks in multiple ways using Python
- Configure activation functions, loss functions, optimizers, hyperparameters, and layers
- Apply neural networks to practical applications such as anomaly detection, automation, cybersecurity, and predictive systems
Key Takeaways
- Neural networks can be implemented in multiple ways using Python, including with Scikit-Learn and PyTorch.
- The type of neural network can be determined based on the type of data being used.
- Designing a neural network involves selecting algorithms, methods, and values for aspects such as activation functions, loss functions, optimizers, hyperparameters, and layers.
- Relevant libraries and frameworks for neural networks include Tensorflow, Keras, and PyTorch, and GPU acceleration can support implementation.
- Neural networks have practical applications including anomaly/threat detection, automation, cybersecurity, and predictive systems.
Frequently Asked Questions
What does this course cover?
The course covers the basics of neural networks, including an introduction to neural networks and related concepts, the various design aspects of neural networks, different types of neural networks, how to implement them in Python, the advantages of different frameworks and implementations, and practical applications.
Will I learn how to implement neural networks in code?
Yes. The course covers how to implement neural networks, specifically in Python, including using Scikit-Learn and PyTorch, and discusses relevant libraries and frameworks such as Tensorflow, Keras, and PyTorch.
What types of neural networks are discussed?
The course covers different types of neural networks, including MLP and convolutional networks, and how to determine the neural network type based on the data type.
What practical applications of neural networks are included?
The course discusses practical applications including anomaly/threat detection, automation, cybersecurity, and predictive systems.
What will I be able to do by the end of the course?
By the end of the course, you will be aware of multiple ways to implement a neural network and how to select algorithms, methods, and values for the various aspects of a neural network.
