In this video I combine all the assembly language functions from the previous videos to create a functional (yet very simple) artificial neural network. It has a single layer of artificial neurons, employing the logistic (sigmoid) activation function. I implemented the forward pass, loss computation (using Mean Square Error MSE), and the backward pass for the backpropagation algorithm. Data is stored in vectors of 32bit floating point numbers. I am using x87 floating point operations. The code is written in x86 64bits assembly language. The test program uses Linux SYSCALLs for writing to the console.

The code is here: https://github.com/ComputingMongoose/...

Previous videos of interest:

Blueprint for an Artificial Neural Network entirely in Assembly Language:
   • Blueprint for an Artificial Neural Ne...  

Vector operations videos (initialization, copy, multiplication, Sigmoid, Sigmoid derivative, Scalar product, Hadamard product, Dot product, Outer product, addition, subtraction):
   • Vectors in assembly language PART1  
   • Vectors in assembly language PART2  
   • Vectors in assembly language PART3 (c...  
   • Vectors PART4 (addition, subtraction)  
   • Vectors outer product in assembly lan...  

Matrix operations:
   • Matrix operations in assembly language  

Mean Squared Error (MSE):
   • Mean squared error (MSE) in assembly ...  

Writing to the Linux console using SYSCALL:
   • Writing to Linux console in 64bit ass...  

My Assembly Language playlist:
   • x86 Assembly Language  

Chapters:
00:00 Introduction
00:50 Input, Output, Internal data
01:40 Forward pass
02:46 Loss
03:05 Backward pass
05:00 Artificial Neural Network code with functions for layerForward, outputLoss, layerBackward
15:15 Main training code and training data
25:55 Build Bash script for compilation
26:44 Running the neural network in training mode
27:23 Analysis of information displayed during network training

#assemblylanguage #ai #artificialintelligence #linux #neuralnetworks #artificialneuralnetwork #artificialneuralnetworks #x86 #64bits #8087