The Hundred-Page Language Models Book
Read it:
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Read it:
https://github.com/aburkov/theLMbook
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Generative AI for beginners by Microsoft
21 Lessons teaching everything you need to know to start building Generative AI applications
Enroll Free: https://github.com/microsoft/generative-ai-for-beginners
21 Lessons teaching everything you need to know to start building Generative AI applications
Enroll Free: https://github.com/microsoft/generative-ai-for-beginners
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Understanding Probability Distributions for Machine Learning with Python
In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python.
This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.
Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/
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In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and #data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right #algorithms, and interpreting outputs of a probabilistic nature, especially when building #models with #machinelearning-friendly programming languages like #Python.
This article unveils key #probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.
Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/
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💰 Price:$20 or ₹1500
✅ 1 Year You.com Pro on Your Mail
💰 Price:$25 or ₹1800
💰Combo Offer:40$
Original Price:200$
How I activate ?
I activate account through voucher codes on your mail for 1 year.
💡 Features Included
✅Advanced AI Models:
• DeepResearch
•GPT-4o, o1, o3 mini(High)
• Deepseek r1[USA Hosted Uncensored]
• Llama 3.1
•Claude 3.5 Sonnet, Claude 3.5 Haiku
•Grok-2(Grok 3 coming too confirmed by its CEO)
•FILE ANALYSIS
•PRO SEARCH
✅Image Generation 🎥
•Flux, DALL-E 3
•Playground v3, Stable Diffusion XL
✔️ What You Get
•1 year of full access.
•A 12-month warranty is included.
💨 This post will be deleted/removed after 24 hours so save my username or contact immediately.
💰 Payment Method: Crypto[LTC or USDT] or UPI
✅ For Inquiry/Purchase DM: @AiChatBoss
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Link: https://amankharwal.medium.com/130-python-projects-with-source-code-61f498591bb
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Find your location on Map using Python
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📁 I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!👇
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Generative vs. discriminative models in ML:
Generative models:
- learn the distribution so they can generate new samples.
- possess discriminative properties, we can use them for classification.
Discriminative models don't have generative properties.
Generative models:
- learn the distribution so they can generate new samples.
- possess discriminative properties, we can use them for classification.
Discriminative models don't have generative properties.
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Pen and Paper Exercises in MachineLearning
Free 211-page PDF: arxiv.org/abs/2206.13446
GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
Free 211-page PDF: arxiv.org/abs/2206.13446
GitHub: https://github.com/michaelgutmann/ml-pen-and-paper-exercises
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It was a challenge - a marathon 300$ to 30.000$ on trading, together with Lisa!
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TensorFlow v2.0 Cheat Sheet
#TensorFlow is an open-source software library for highperformance numerical computation. Its flexible architecture enables to easily deploy computation across a variety of platforms (CPUs, GPUs, and TPUs), as well as mobile and edge devices, desktops, and clusters of servers. TensorFlow comes with strong support for machine learning and deep learning.
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Machine Learning Glossary
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more.
Link: https://ml-cheatsheet.readthedocs.io/en/latest/index.html
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