Artificial Neural Networks: From Theory to Code
Artificial Neural Networks: From Theory to Code
Dive into the f Artificial Neural Networks (ANNs) in this comprehensive video. Drawing inspiration from the human brain, ANNs serve as the foundational building blocks for deep learning models.
In this session, I explore how ANNs aim to replicate human-like processing by transforming simple line equations from
y = mx + c to
o = wx + b .
I delve into the intricacies of matrix representation and equation representation, providing a clear understanding of how these networks function.
Towards the end, I walk you through a basic ANN code, demonstrating the practical application of the concepts discussed. For those interested in getting hands-on, the code used in this video is available on GitHub.
https://github.com/sajjadrahman56/Learn-ML...
Whether you're a beginner just starting out or an experienced professional looking to refresh your knowledge, this video serves as a comprehensive guide to understanding ANNs.
✌ Connect with me :
GitHub : https://github.com/sajjadrahman56
🐤Twitter: https://twitter.com/sajjadrahman56
#ArtificialNeuralNetworks #DeepLearning #MachineLearning #ANN #AI
#datascience #CodingTutorial #MatrixRepresentation #human #humanbrain #ann #Keras #TensorFlow #GoogleAI #JamAI #GemmaAI #GenAI #LLM #Tech #AICommunity #NeuralNetworks #PythonProgramming #DeepLearningModels #OpenSource #GitHub #CodingCommunity #ConsoleProject #medicalsystem #EquationRepresentation#cprogramming #cosoleproject #medicalsystem #programming c #programing #cse #cseengineering