Book: DATA MINING
FOR BUSINESS ANALYTICS(Concepts, Techniques, and Applications in Python)
Authors: GALIT SHMUELI, PETER C., BRUCE PETER, and GEDECK NITIN R. PATEL
ISBN: Null
year: 2019
pages: 681
Tags: #Python #datamining #business
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Book: Designing Machine Systems An Iterative Process for Production-Ready Applications
Authors: Chip Huyen
ISBN: 978-1-098-10796-3
year: 2022
pages: 463
Tags: #Python #datamining #ML
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Book: Pandas for Everyone
Authors: D A N I E L Y. C H E N
ISBN: 978-0-13-789115-3
year: 2023
pages: 1148
Tags: #Pandas #python
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Book: Applied Recommender Systems with Python
Authors: Akshay Kulkarni Adarsha ,Shivananda Anoosh Kulkarni
V, Adithya Krishnan
ISBN: 978-1-4842-8954-9
year: 2022
pages: 257
Tags: #Recommender_system #python
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Book: Python for you and me
Release 0.5.beta1
Authors: Kushal Das
ISBN: Null
year: 2023
pages: 175
Tags: #Python #Code
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Book: Deep Learning for Natural Language Processing (Creating Neural Networks with Python)
Authors: Palash Goyal, Sumit Pandey, Karan Jain
ISBN: 978-1-4842-3685-7
year: 2018
pages: 290
Tags: #NLP #DL #Python #Code
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A Collection of 25 Awesome Python Scripts (mini projects)
#Python #Mini_Projects
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Data Science Cheat Sheet
Python Regular Expressions
#Python
#RE
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Book: Intro to Python® for Computer Science and Data Science
Authors: Paul Deitel • Harvey Deitel
ISBN: 1-292-36490-4
year: 2022
pages: 882
Tags:#Python #Computer_science #Data_Science
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Book: Graph Data Modeling in Python
Authors: Gary Hutson, Matt Jackson
ISBN: 978-1-80461-803-5
year: 2023
pages: 236
Tags:#Python #Graph #Data_Science
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Book: MODELING AND SIMULATION
IN PYTHON AN INTRODUSTENNINGERSCIENTISTS
Authors: Allen B. Downey
ISBN: 978-1-7185-0217-8
year: 2023
pages: 344
Tags:#Python #"Modeling
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Book: Test Your Skills In Python
SECOND EDITION
Authors: SHIVANI GOEL
ISBN: 978-93-5551-181-2
year: 2023
pages: 308
Tags:#Python
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Book: Mathematics of Deep Learning
Authors: Leonid Berlyand and Pierre-Emmanuel Jabin
ISBN: 978-3-11-102431-8
year: 2023
pages: 308
Tags:#Python
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Book: Blockchain Tethered AI
Trackable, Traceable Artificial Intelligence and Machine Learning
Authors: Karen Kilroy, Lynn Riley, and Deepak Bhatta
ISBN: 978-1-098-13048-0
year: 2023
pages: 307
Tags:#Python #Blockchain
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Book: Python for Scientists
Third Edition
Authors: JOHN M. STEWART
ISBN: 978-1-119-82094-9 (ebk)
year: 2023
pages: 301
Tags:#Python
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Book: Foundation Python for Data Scientist
Authors: Kennedy R
ISBN: Null
year: 202
pages: 686
Tags:#Python
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Book: 📚Python code for Artificial Intelligence Foundations of Computational Agents
Authors: David L. Poole and Alan K. Mackworth
year: 2024
pages: 392
Tags: #Python
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The first channel on Telegram that offers exciting questions, answers, and tests in data science, artificial intelligence, machine learning, and programming languages.

#interviews #datascience #python

https://t.iss.one/DataScienceQ

Practical Statistics for Data Scientists
50+ Essential Concepts Using R and Python
#Python #Book

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Executable Code Actions Elicit Better LLM Agents

1 Feb 2024 · Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji

Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating #JSON or text in a pre-defined format, which is usually limited by constrained action space (e.g., the scope of pre-defined tools) and restricted flexibility (e.g., inability to compose multiple tools). This work proposes to use executable Python code to consolidate LLM agents' actions into a unified action space (CodeAct). Integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. Our extensive analysis of 17 LLMs on API-Bank and a newly curated benchmark shows that CodeAct outperforms widely used alternatives (up to 20% higher success rate). The encouraging performance of CodeAct motivates us to build an open-source #LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language. To this end, we collect an instruction-tuning dataset CodeActInstruct that consists of 7k multi-turn interactions using CodeAct. We show that it can be used with existing data to improve models in agent-oriented tasks without compromising their general capability. CodeActAgent, finetuned from Llama2 and Mistral, is integrated with #Python interpreter and uniquely tailored to perform sophisticated tasks (e.g., model training) using existing libraries and autonomously self-debug.

Paper: https://arxiv.org/pdf/2402.01030v4.pdf

Codes:
https://github.com/epfllm/megatron-llm
https://github.com/xingyaoww/code-act

Datasets: MMLU - GSM8K - HumanEval - MATH

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