Neural Networks from Scratch in JAVA : Video#2 Matrix Functions Code
Neural Networks from Scratch in JAVA
Completely using an Object Orientated Approach
#Vedio1: • Neural Networks from Scratch in JAVA Compl...
#Vedio2: Math Operations
Neural Network Math’s Operations Class
1) Activation functions – Sigmoid Function, Tanh Functions
2) Matrix Multiplication
3) Scalar Multiplication to matrix
4) Matrix Addition
5) Matrix Subtraction
6) Matrix Transpose
7) Matrix Normalization
Complete code of NNMath class-
http://86amruth.blogspot.com/2020/04/...
Vedio#1: Introduction and Neural Layer Class
• Neural Networks from Scratch in JAVA Compl...
• Not need to include complete libraries like NumPy, TensorFlow or Pytrouch
• Writing a lightweight and server independent application.
• More Experimentation.
Java V/s Python
• Code Reusability in Java Best as OOPs
• Java handles concurrency better than Python, Python is more sequential
• Speed – Using JVM for Just in Time compilation
• Platform independent - Write once run anywhere with JVM (Java Virtual machine)
Neural Layer Class
Neural Layer – Collection of a set of neurons.
Neurons – Multi Input , Single Out put ( i/p * Weights) =Activation Function
Activation Function – Bounding the values.
Error Correction – Back Propagation = Covered In next video
