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Inpainting and outpainting are popular image editing techniques. You have seen how to perform inpainting and outpainting using the WebUI. You can do the same using code as well. In this post, you will see how you can use the diffusers library from Hugging Face to run Stable Diffusion pipeline to perform inpainting and outpainting. …
Collaborating on a machine learning project is a bit different from collaborating on a traditional software project. In a machine learning project, engineers are working with data, models, and source code. Additionally, they are also sharing features, model experiment results, and pipelines. You can’t just use any code-sharing platform for a machine learning project; you …
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There are many ways you can access Stable Diffusion models and generate high-quality images. One popular method is using the Diffusers Python library. It provides a simple interface to Stable Diffusion, making it easy to leverage these powerful AI image generation models. The diffusers lowers the barrier to using cutting-edge generative AI, enabling rapid experimentation …
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A loss function in machine learning is a mathematical formula that calculates the difference between the predicted output and the actual output of the model. The loss function is then used to slightly change the model weights and then check whether it has improved the model’s performance. The goal of machine learning algorithms is to …
If you’re reading this article, I assume you already know what machine learning is. But just for a quick refresher, it’s simply making computers smart enough to do jobs that humans used to do, for example, taking attendance using facial recognition. Anyway, moving on to our main discussion, I know there are a lot of …
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The ability of LLMs to execute commands through plain language (e.g. English) has enabled agentic systems that can complete a user query by orchestrating the right set of tools (e.g. ToolFormer, Gorilla). This, along with the recent multi-modal efforts such as the GPT-4o or Gemini-1.5 model, has expanded the realm of possibilities with AI agents. …
Introduction The process of deploying machine learning models is an important part of deploying AI technologies and systems to the real world. Unfortunately, the road to model deployment can be a tough one. The process of deployment is often characterized by challenges associated with taking a trained model — the culmination of a lengthy data-preparation …
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Data science uses mathematics to analyze data, distill information, and tell a story. The result of data science may be just to rigorously confirm a hypothesis, or to discover some useful property from the data. There are many tools you can use in data science, from basic statistics to sophisticated machine learning models. Even the …
Introduction Imperfect data is the norm rather than the exception in machine learning. Comparably common is the binary class imbalance when the classes in a trained data remains majority/minority class, or is moderately skewed. Imbalanced data can undermine a machine learning model by producing model selection biases. Therefore in the interest of model performance and …
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Introduction Classification algorithms are at the heart of data science, helping us categorize and organize data into pre-defined classes. These algorithms are used in a wide array of applications, from spam detection and medical diagnosis to image recognition and customer profiling. It is for this reason that those new to data science must know about …
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