10. Introduction to Neural Networks
Neural networks are computational models that simulate the human brain to process information, forming the foundation of deep learning in AI. They consist of interconnected layers of neurons, which learn from large datasets to perform tasks such as image recognition and language processing. While highly effective, neural networks have limitations, including requiring substantial computational resources and large amounts of data.
Enroll to start learning
You've not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Neural networks mimic human brain structure and function.
- They are composed of input, hidden, and output layers.
- Neural networks have various applications, including image and speech recognition.
Key Concepts
- -- Neuron
- The basic unit in a neural network that receives inputs, processes them, and produces an output.
- -- Weights
- The strength of the connection between neurons.
- -- Bias
- A constant added to the input to adjust the output.
- -- Activation Function
- A function that decides whether a neuron should be activated or not.
- -- Feedforward Neural Network (FNN)
- A neural network where information moves in one direction.
- -- Convolutional Neural Network (CNN)
- A specialized neural network type designed to process and analyze image data.
- -- Recurrent Neural Network (RNN)
- A type that has memory and is suitable for processing sequences.
Additional Learning Materials
Supplementary resources to enhance your learning experience.