Explore the fundamental concepts of biological and artificial neural networks, computational models that can be implemented with neural networks, and how to implement neural networks using Python.

Getting Started with Neural Networks: Biological & Artificial Neural Networks

discover the key concepts covered in this course

identify the characteristics of biological neural networks that inspired artificial neural networks

list the essential components of biological neural networks and describe the signal processing mechanism of biological neural networks

describe essential components of artificial neural networks and their capabilities

recognize layered architectural patterns that can be used to implement neural networks

classify the various computational models that can be implemented using the neural networks paradigm

describe the interconnection between neurons involving weights and fixed weights

describe threshold functions and the basic logic gates of AND, OR, and XNOR

implement neural networks using Python and the core libraries provided by Python for neural networks

create a neural network model using Python, Keras, and TensorFlow

list prominent use cases of implementing neural networks

recall the essential components of artificial neural networks, list the prominent use cases of neural networks, and implement neural networks using Python