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Unpacking Yolov8: Ultralytics’ Viral Computer Vision Masterpiece

Unite.AI

Yolov2 : The next version, released in 2016, presented a top performance on benchmarks like PASCAL VOC and COCO and operates at high speeds (67-40 FPS). The YOLOv8 model can be finetuned with your Python code or through the command line interface (CLI). The implementation of both these methods remains the same. pt’ file.

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The End of Programming as We Know It

Flipboard

New interpreted programming languages like Python and JavaScript became dominant. Tim OReilly, Managing the Bots That Are Managing the Business , MIT Sloan Management Review , May 21, 2016 In each of these waves, old skills became obsolescentstill useful but no longer essentialand new ones became the key to success.

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How to Detect the Trend in the Time Series Data and Detrend in Python

Towards AI

To demonstrate the trend, we will use Pollution US 2000 to 2016 data from Kaggle. The change of direction in the data for a sustained period can be called a trend. It will be clearer with the examples below. Please feel free to download the dataset from this link: U.S. csv') This dataset is pritty big.

Python 115
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Entity Recognition with LLM: A Complete Evaluation

Towards AI

SpaCy is a language processing library written in Python and Cython that has been well-established since 2016. With little or no examples, they are able to perform language processing operations such as classification, sentiment analysis, concept extraction, named entity recognition, translation, and more.

LLM 116
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Replit, the web-based IDE developing a GitHub Copilot competitor, raises $100M

Flipboard

.” Based in San Francisco, Replit was co-founded by programmers Amjad Masad, Faris Masad and designer Haya Odeh in 2016. Image Credits: Replit Replit offers an online, collaborative IDE that supports a range of programming languages, including JavaScript, Python, Go and C++.

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Improving air quality with generative AI

AWS Machine Learning Blog

The solution harnesses the capabilities of generative AI, specifically Large Language Models (LLMs), to address the challenges posed by diverse sensor data and automatically generate Python functions based on various data formats. It generates a Python function to convert data frames to a common data format.

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LLM continuous self-instruct fine-tuning framework powered by a compound AI system on Amazon SageMaker

AWS Machine Learning Blog

We use DSPy (Declarative Self-improving Python) to demonstrate the workflow of Retrieval Augmented Generation (RAG) optimization, LLM fine-tuning and evaluation, and human preference alignment for performance improvement. Examples are similar to Python dictionaries but with added utilities such as the dspy.Prediction as a return value.

LLM 81