Reinforcement learning (RL) is a subfield of machine learning where an agent learns to make decisions by interacting with its environment rather than relying solely on pre-existing data. It is an area that blends trial-and-error learning with feedback from actions to improve future performance. In this blog, we will explore 5 free courses that I …
Stable Diffusion is trained on LAION-5B, a large-scale dataset comprising billions of general image-text pairs. However, it falls short of comprehending specific subjects and their generation in various contexts (often blurry, obscure, or nonsensical). To address this problem, fine-tuning the model for specific use cases becomes crucial. There are two important fine-tuning techniques for stable …
Deep learning models achieve state-of-the-art performance in several computer vision and natural language processing tasks. If you want to become proficient in deep learning, you should first understand how neural networks work and then proceed to explore the different types and neural network architectures for specific tasks. To help you get started, we’ve compiled a …
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After completing a course and mastering the essentials of machine learning, it is time to start building machine learning models using real-world datasets. While accessing most real-world datasets can be expensive, platforms like Kaggle offer access to the latest datasets for free on a wide variety of machine learning tasks. In this blog, we will …
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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. …