Broadcasting, as done by Python’s scientific computing library NumPy, involves dynamically extending shapes so that arrays of different sizes may be passed to operations that expect conformity – such as adding or multiplying elementwise. In NumPy, the way broadcasting works is specified exactly; the same rules apply to TensorFlow operations. For anyone who finds herself, occasionally, consulting Python code, this post strives to explain.
Table of Contents CycleGAN: Unpaired Image-to-Image Translation (Part 3) Configuring Your Development Environment Need Help Configuring Your Development Environment? Project Structure Implementing CycleGAN Training Implementing Training Callback Implementing Data Pipeline and Model Training Perform Image-to-Image Translation Summary Citation Information CycleGAN:…
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Machine learning advancements lead to new ways to train models, as well as deceive them. This article discusses ways to train and defend against attacks.
Since the release of ChatGPT, Large language models (LLMs) have received a huge amount of attention in both industry and the media; resulting in an…
What is Attention, and why is it used in state-of-the-art models? This article discusses the types of Attention and walks you through their implementations.
This article discusses GPT-2 and BERT models, as well using knowledge distillation to create highly accurate models with fewer parameters than their teachers
