DeepTrainer roadmap changes

More than a year passed since the last productive post, since then I have got busy with a daytime job. In the meantime I have been thinking a lot where I could define the main selling point of this project if I wanted to make it to generate income for me. I stopped making the […]

Dynamic Graph Architecture to Enable Composite Networks

I had been thinking for a while about the most flexible way to add Convolutional layers to my existing API when I read an article about the so-called Deep Residual Learning. Roughly speaking Residual Learning requires a network architecture in which each layer consists of two layers, duplicates of the same amount of neurons, and […]

Artificial Intelligence Fight VI. – Some Unexpected Improvements

Since the last AI Fight article I did not make conscious efforts to improve the performance of the algorithms, I have been busy with updating the existing interfaces/application, and with creating new interfaces. I have rewritten all test harnesses from scratch, and I have added a new WebAPI interface. Also I started experimenting with an […]

New XNNS file format to save and load neural networks

I have been reading about the ONNX file format recently that has been created internally by Google, but before I delve myself into protocol buffers I still needed an easily readable (read: debuggable) file format to exchange neural network states. I know that eventually I will have to end up supporting ONNX so I did […]

Why DeepTrainer?

Recently I’ve been reading quite a lot about activation functions and Neural Networks in general and I think I found a good answer to a question that has been bugging me (and others who know what I am working on) ever since I started working on my own deep learning framework. I’ve had conversations with […]