I have an extensive background in deep neural networks. I have previously worked a senior research engineer in deep learning where I primarily dealt with working with research papers and implementing them to suit our use-cases. All my previous roles have been research based engineering roles where I research different architectures and methods and built systems to deploy those systems at scale.
I understand all different kinds of architectures quite well having developed and implemented a variety of deep learning models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and VAEs (Variational Auto-encoders) to name a few.
I've designed my own custom neural network architectures from scratch His experience covers a wide range of applications, including object detection, image de-noising, image classification, and one-shot learning methods.
I have expertise in utilising popular frameworks like PyTorch, TensorFlow, and also JAX lately and know how to optimise models for performance and deploy them in real-world scenarios at scale.
