Getting Started with Neural Networks: Biological & Artificial Neural Networks

Learners can explore fundamental concepts of biological and artificial neural networks computational models that can be implemented with neural networks and how to implement neural networks with Python in this 12-video course. Begin with a look at characteristics of machine learning biological neural networks that inspired artificial neural networks. Then explore components of biological neural networks and the signal processing mechanism. Next take a look at the essential components of the structure of artificial neural networks; learn to recognize the layered architecture of neural networks; and observe how to classify various computational models that can be implemented by using neural networks paradigm. Examine neurons connectivity by describing the interconnection between neurons involving weights and fixed weights. This leads on to threshold functions in neural networks and the basic logic gates of AND OR and XNOR. Implement neural networks by using Python and the core libraries provided by Python for neural networks; create a neural network model using Python Keras and TensorFlow and finally view prominent neural network use cases. The concluding exercise involves implementing neural networks.