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Using Generative AI to Build Generative AI

O'Reilly Media

In this post I want to talk about using generative AI to extend one of my academic software projectsthe Python Tutor tool for learning programmingwith an AI chat tutor. Python Tutor is mainly used by students to understand and debug their homework assignment code step-by-step by seeing its call stack and data structures.

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Michael I. Jordan of Berkeley on Learning-Aware Mechanism Design

ODSC - Open Data Science

Rumelhart Prize in 2015, and the ACM/AAAI Allen Newell Award in 2009. He received the Ulf Grenander Prize from the American Mathematical Society in 2021, the IEEE John von Neumann Medal in 2020, the IJCAI Research Excellence Award in 2016, the David E.

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Top 10 Generative AI Companies Revealed

Towards AI

Intelligent Medical Objects 👉Industry domain: AI, Health Tech, IT, NLP, Software, Analytics, Generative AI 👉Location: 3 offices 👉Year founded: 1994 👉Programming languages deployed: Angular, C#, SQL, Scikit, TensorFlow, Spark, GitHub, R, Python 👉Benefits: Flexible time off, family medical leave, pet insurance, (..)

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A Practical Guide for identifying important features using Python

Mlearning.ai

Identifying important features using Python Introduction Features are the foundation on which every machine-learning model is built. Nonetheless, features are an essential ingredient in building an ML model. This covers unsupervised, supervised, self-supervised, decision-making, and even graph ML. XGBoost, LightGBM). Menze, B.H.,

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Amazon SageMaker built-in LightGBM now offers distributed training using Dask

AWS Machine Learning Blog

Amazon SageMaker provides a suite of built-in algorithms , pre-trained models , and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. They’re available through the SageMaker Python SDK. 1 5329 5414 0.937 0.947 65.6 2 3175 3294 0.94

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

JumpStart helps you quickly and easily get started with machine learning (ML) and provides a set of solutions for the most common use cases that can be trained and deployed readily with just a few steps. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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