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We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.
In addition, you'll find many other useful materials on machine learning on the site.
Find and use it β https://mlu-explain.github.io/neural-networks/
tags: #AI #ML #PYTHON
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Forwarded from Learn Python Hub
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Data scientists are in high demand right now: there's just too much data to analyze.
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://t.iss.one/Python53π
In this course, Tatev and Vae teach #Python for #DataScience.
You'll be doing projects and exploring EDA, A/B testing, BI, and more.
https://t.iss.one/Python53
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Data Science Roadmap.pdf
15.5 MB
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β This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
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π #DataScience #DataScience #pytorch #python #Roadmap
https://t.iss.one/CodeProgrammer
β This roadmap is exactly the secret recipe you need to get out of confusion and know how to step-by-step prepare yourself for the job market.
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π You will learn everything from machine learning and advanced algorithms to precise model evaluation.
π Get familiar with neural networks, generative artificial intelligence, and language models to have a voice in today's modern world.
π§ How to build real projects and portfolios that are exactly what hiring managers and big companies are looking for.
π #DataScience #DataScience #pytorch #python #Roadmap
https://t.iss.one/CodeProgrammer
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1. Makes the first letter capitalized
.capitalize()
2. Lowers or raises the case of a string
.lower()
.upper()3. Centers the string with symbols around it: 'Python' β 'Python'
.center(10, '*')
4. Counts the occurrences of a specific character
.count('0')5. Finds the positions of specified characters
.find()
.index()
6. Searches for a desired object and replaces it
.replace()
7. Splits the string, removing the split point from it
.split()8. Checks what the string consists of
.isalnum()
.isnumeric()
.islower()
.isupper()tags: #useful
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Forwarded from PyData Careers
Stop using
In Python, when you write:
you're not directly checking if
Yes, in many cases, the result will be the same as for the code:
But the behavior of these two variants is different, and this difference is important.
When you use:
Python calls the
If
Example:
Here, it can be seen that
Therefore, when using
On the other hand, when you write:
you're using the
The
β€οΈ Therefore, it is always recommended, and this is best practice, to use
π https://t.iss.one/DataScienceQ
if obj == None, use if obj is NoneIn Python, when you write:
obj == None
you're not directly checking if
obj is the value None. Instead, you're asking if the object is equal to None.Yes, in many cases, the result will be the same as for the code:
obj is None
But the behavior of these two variants is different, and this difference is important.
When you use:
obj == None
Python calls the
__eq__ method on the object. That is, the object itself decides what it means to be "equal to None". And this method can be overridden.If
obj is an instance of a class in which __eq__ is implemented so that when compared with None, it returns True (even if the object is not actually None), then obj == None may mistakenly give True.Example:
class Weird:
def __eq__(self, other):
return True # Always asserts that it's equal
obj = Weird()
print(obj == None) # True
print(obj is None) # False
Here, it can be seen that
obj == None returns True due to the custom behaeqf the __eq__ operator in the class.Therefore, when using
obj == None, the result is not always predictable.On the other hand, when you write:
obj is None
you're using the
is operator, which cannot be overridden. This means that the result will always be the same and predictable.The
is operator checks the identity of objects, that is, whether two references point to the same object. Since None is a singleton (the only instance), obj is None is the correct and most efficient way to perform such a check.obj is None instead of obj == None for predictability and efficiency.Please open Telegram to view this post
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Forwarded from .
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Inside:
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tags: #ML #DEEPLEARNING #AI
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Forwarded from Data Analytics
#Polars Cheat Sheet with example
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://t.iss.one/DataAnalyticsXπ΄
Learn and Run online: https://colab.research.google.com/github/FranzDiebold/polars-cheat-sheet/blob/main/polars-cheat-sheet.ipynb
https://t.iss.one/DataAnalyticsX
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Google
polars-cheat-sheet.ipynb
Run, share, and edit Python notebooks
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If you work with AI, agents, or APIs, this is the foundation that developers in top companies are currently going through.
https://anthropic.skilljar.com/claude-with-the-anthropic-api
https://anthropic.skilljar.com/introduction-to-model-context-protocol
https://anthropic.skilljar.com/claude-in-amazon-bedrock
https://anthropic.skilljar.com/claude-with-google-vertex
https://anthropic.skilljar.com/model-context-protocol-advanced-topics
https://anthropic.skilljar.com/claude-code-in-action
tags: #courses #ai
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If you want to understand AI not through "vacuum" courses, but through real open-source projects - here's a top list of repos that really lead you from the basics to practice:
1) Karpathy β Neural Networks: Zero to Hero
The most understandable introduction to neural networks and backprop "in layman's terms"
https://github.com/karpathy/nn-zero-to-hero
2) Hugging Face Transformers
The main library of modern NLP/LLM: models, tokenizers, fine-tuning
https://github.com/huggingface/transformers
3) FastAI β Fastbook
Practical DL training through projects and experiments
https://github.com/fastai/fastbook
4) Made With ML
ML as an engineering system: pipelines, production, deployment, monitoring
https://github.com/GokuMohandas/Made-With-ML
5) Machine Learning System Design (Chip Huyen)
How to build ML systems in real business: data, metrics, infrastructure
https://github.com/chiphuyen/machine-learning-systems-design
6) Awesome Generative AI Guide
A collection of materials on GenAI: from basics to practice
https://github.com/aishwaryanr/awesome-generative-ai-guide
7) Dive into Deep Learning (D2L)
One of the best books on DL + code + assignments
https://github.com/d2l-ai/d2l-en
Save it for yourself - this is a base on which you can really grow into an ML/LLM engineer.
#Python #datascience #DataAnalysis #MachineLearning #AI #DeepLearning #LLMS
https://t.iss.one/CodeProgrammer
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π Generative AI Training for Beginners
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://t.iss.one/CodeProgrammer
A course from Microsoft with 21 lessons covering the basics of creating applications based on generative AI. Each lesson includes theory and practical examples in Python and TypeScript, allowing you to learn at a comfortable pace.
π Key features:
- 21 lessons on generative #AI
- Support for Python and TypeScript
- Lessons with theory and practical tasks
- Additional resources for in-depth study
- Multilingual support
π GitHub: https://github.com/microsoft/generative-ai-for-beginners
#python #LLMS #generative_Ai
https://t.iss.one/CodeProgrammer
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Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.
We gain useful knowledge β
https://developer.nvidia.com/join-nvidia-developer-program
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Here's everything important: video processing, app development, robotics, and much more. An electronic certificate is issued upon completion of the training.
We gain useful knowledge β
https://developer.nvidia.com/join-nvidia-developer-program
https://t.iss.one/CodeProgrammer
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β‘οΈ MIT has released a full course on Deep Learning - for free
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) β one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
https://t.iss.one/CodeProgrammerβ
MIT OpenCourseWare has published the course 6.7960 Deep Learning (Fall 2024) β one of the most relevant and practical university courses on modern deep learning.
It includes full-fledged lectures at a top-university level, available for free.
What's in the course
- Fundamentals of deep learning and architectures
- Transformers and modern models
- Generative AI
- Self-supervised learning
- Scaling laws
- Diffusion and generative models
- RL and reinforcement learning
- Practical analyses of modern approaches
The lectures are led by MIT professors and researchers working with cutting-edge technologies.
Why it's valuable
This is not a basic course for beginners.
This is material at the level of:
- ML engineers
- researchers
- developers of AI systems
The course reflects the current state of the industry and explains how people who create modern models think.
It's perfect if you:
- already know Python and the basics of ML
- want to transition to Deep Learning
- work with LLMs / AI
- want a systematic understanding instead of individual tutorials
If you want FAANG / Research-level knowledge - learn from MIT.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/video_galleries/lecture-videos/
https://t.iss.one/CodeProgrammer
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Pandas vs. Polars: A Complete Comparison of Syntax, Speed, and Memory
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
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Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.iss.one/CodeProgrammer πΊ
Need help choosing the right #Python dataframe library? This article compares #Pandas and #Polars to help you decide.
If you've been working with data in Python, you've almost certainly used pandas. It's been the go-to library for data manipulation for over a decade. But recently, Polars has been gaining serious traction. Polars promises to be faster, more memory-efficient, and more intuitive than pandas. But is it worth learning? And how different is it really?
In this article, we'll compare pandas and Polars side-by-side. You'll see performance benchmarks, and learn the syntax differences. By the end, you'll be able to make an informed decision for your next data project.
Read: https://www.kdnuggets.com/pandas-vs-polars-a-complete-comparison-of-syntax-speed-and-memory
https://t.iss.one/CodeProgrammer πΊ
β€8π2π2
Forwarded from Machine Learning
10 GitHub Repositories to Master System Design
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
https://t.iss.one/DataScienceMβ
Want to move beyond drawing boxes and arrows and actually understand how scalable systems are built? These GitHub repositories break down the concepts, patterns, and real-world trade-offs that make great system design possible.
Most engineers encounter system design when preparing for interviews, but in reality, it is much bigger than that. System design is about understanding how large-scale systems are built, why certain architectural decisions are made, and how trade-offs shape everything from performance to reliability. Behind every app you use daily, from messaging platforms to streaming services, there are careful decisions about databases, caching, load balancing, fault tolerance, and consistency models.ο»Ώ
What makes system design challenging is that there is rarely a single correct answer. You are constantly balancing cost, scalability, latency, complexity, and future growth. Should you shard the database now or later? Do you prioritize strong consistency or eventual consistency? Do you optimize for reads or writes? These are the kinds of questions that separate surface-level knowledge from real architectural thinking.
The good news is that many experienced engineers have documented these patterns, breakdowns, and interview strategies openly on GitHub. Instead of learning only through trial and error, you can study real case studies, curated resources, structured interview frameworks, and production-grade design principles from the community.
In this article, we review 10 GitHub repositories that cover fundamentals, interview preparation, distributed systems concepts, machine learning system design, agent-based architectures, and real-world scalability case studies. Together, they provide a practical roadmap for developing the structured thinking required to design reliable systems at scale.
Read: https://www.kdnuggets.com/10-github-repositories-to-master-system-design
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Pandas-Cheat-Sheet.pdf
2.7 MB
This cheat sheetβpart of our Complete Guide to #NumPy, #pandas, and #DataVisualizationβoffers a handy reference for essential pandas commands, focused on efficient #datamanipulation and analysis. Using examples from the Fortune 500 Companies #Dataset, it covers key pandas operations such as reading and writing data, selecting and filtering DataFrame values, and performing common transformations.
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandasβ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandasβ versatile data manipulation tools in your workflow.
https://t.iss.one/CodeProgrammer
You'll find easy-to-follow examples for grouping, sorting, and aggregating data, as well as calculating statistics like mean, correlation, and summary statistics. Whether you're cleaning datasets, analyzing trends, or visualizing data, this cheat sheet provides concise instructions to help you navigate pandasβ powerful functionality.
Designed to be practical and actionable, this guide ensures you can quickly apply pandasβ versatile data manipulation tools in your workflow.
https://t.iss.one/CodeProgrammer
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