Master #LLM fine-tuning with all of our best practices in our new guidebook: Benefits of #FineTuning and when to do it How to prepare your #data set What it takes to manage training + serving #infra How to reliably and #efficiently fine-tune https://
pbase.ai/3wrZlA8
MACHINE LEARNING
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Master LLM Fine-tuning: Best Practices Guidebook
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Top Python Libraries for Data Analytics Tasks
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Python powers data analytics, and this infographic lays out the top libraries for each task! .
For more tech tips, follow @ingliguori
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Combining Offline and Continual Learning for Dynamic AI Models
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The constantly changing nature of the world is a significant challenge for AI models, particularly when it causes a disconnect between training and input data. Learn how we’ve addressed this by combining the strengths of offline and continual learning ↓
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The Importance of Anomaly Detection in Security and Manufacturing
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The Importance of Anomaly Detection Systems that detect anomalies are very critical in areas like security and manufacturing. There are a variety of ways to detect anomalies and platforms like Abacus AI have a wide selection at scale. An interesting use-case of this is
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Mixtures of Experts Unlock Parameter Scaling for Deep RL
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Mixtures of Experts Unlock Parameter Scaling for Deep RL Obando-Ceron et al.: https://
arxiv.org/abs/2402.08609 #ArtificialIntelligence #DeepLearning #ReinforcementLearning -
7B Model Achieves 1M Context Window with RingAttention
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7B model, 1M context window! Lots of results over long token windows; this looks very promising. https://
largeworldmodel.github.io RingAttention is what makes this works, summary generated by GPT: Ring Attention is a new method that reduces the memory requirements of Transformers. It -
Python Machine Learning MLOps CV NLP LLMs Daily Tutorials
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If you are interested in: – Python – Machine Learning – MLOps – CV/NLP – LLMs Find me → @Sumanth_077 Everyday, I share tutorials on above topics! Like/RT the first tweet to help this reach more people!
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Model Pipeline: 3-Step Process for ML Development
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Finally here of how it creates the Model Pipeline which is a 3-step process. 1. Pre-processing and cleaning 2. Feature Engineering 3. Model Building and Training Check the Below Image
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Train Predictive Models with Lightwood: Default and Custom Mixers
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Once you get Python Code for the Problem you can then train the model on the data and run predictions. There are many default available predictive models in Lightwood called "Mixers" but it also supports you to create your custom mixers. Check this: https://
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Pandas to Python Code Generation via JSON-AI Workflow
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Let's see how this works: Load data in Pandas and define a "ProblemDefinition" via dictionary specifying the name of the target column. Now Create a JSON-AI syntax using "json_ai_from_problem" function From Json AI generate a python code using "code_from_json_ai"