PyTorch

before LLMs became hyped, there was an separating Machine Learning frameworks from Deep Learning frameworks. The talk was targeting Scikit-Learn, XGBoost, and similar for ML, while PyTorch and TensorFlow dominated the scene...

Welcome back to the Tiny Giant series — a series where I share what I learned about MobileNet architectures. Up to now two articles I covered MobileNetV1 and MobileNetV2. Take a look at references ...

, slightly optimisation goes a great distance. Models like GPT4 cost greater than $100 tens of millions to coach, which makes a 1% efficiency gain price. A robust strategy to optimise the efficiency of...

After we speak about attention in computer vision, one thing that probably involves your mind first is the one utilized in the Vision Transformer (ViT) architecture. Actually, that’s not the one attention mechanism we've...

world of deep learning training, the role of the ML developer will be likened to that of the conductor of an orchestra. Just as a conductor must time the entry of every instrument...

in fashion. DeepSeek-R1, Gemini-2.5-Pro, OpenAI’s O-series models, Anthropic’s Claude, Magistral, and Qwen3 — there's a brand new one every month. Once you ask these models a matter, they go right into a ...

ninth in our series on performance profiling and optimization in PyTorch aimed toward emphasizing the critical role of performance evaluation and optimization in machine learning development. Throughout the series we've reviewed a wide selection of practical...

in the info input pipeline of a machine learning model running on a GPU may be particularly frustrating. In most workloads, the host (CPU) and the device (GPU) work in tandem: the CPU...