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#LSTMs made AI remember before #Transformers took over
here’s the 15-step by-hand ✍️ guide
you can download: https://www.byhand.ai/p/26-lstm
https://t.iss.one/CodeProgrammer
here’s the 15-step by-hand ✍️ guide
you can download: https://www.byhand.ai/p/26-lstm
https://t.iss.one/CodeProgrammer
❤7
Grab these free AI courses before they get paywalled:
𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true
𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536
𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05
𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity
𝟲. Prompt Engineering Pro:
https://learnprompting.org
𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554
𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning
𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/
𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat
𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/
𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true
𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/
𝟭𝟰. AWS GenAI Foundation:
https://coursera.org/learn/generative-ai-with-llms
https://t.iss.one/CodeProgrammer🔰
𝟭. Prompt Engineering Basics:
https://skillbuilder.aws/search?searchText=foundations-of-prompt-engineering&showRedirectNotFoundBanner=true
𝟮. ChatGPT Prompts Mastery:
https://deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
𝟯. Intro to Generative AI:
https://cloudskillsboost.google/course_templates/536
𝟰. AI Introduction by Harvard:
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05
𝟱. Microsoft GenAI Basics:
https://linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity
𝟲. Prompt Engineering Pro:
https://learnprompting.org
𝟳. Google’s Ethical AI:
https://cloudskillsboost.google/course_templates/554
𝟴. Harvard Machine Learning:
https://pll.harvard.edu/course/data-science-machine-learning
𝟵. LangChain App Developer:
https://deeplearning.ai/short-courses/langchain-for-llm-application-development/
𝟭𝟬. Bing Chat Applications:
https://linkedin.com/learning/streamlining-your-work-with-microsoft-bing-chat
𝟭𝟭. Generative AI by Microsoft:
https://learn.microsoft.com/en-us/training/paths/introduction-to-ai-on-azure/
𝟭𝟮. Amazon’s AI Strategy:
https://skillbuilder.aws/search?searchText=generative-ai-learning-plan-for-decision-makers&showRedirectNotFoundBanner=true
𝟭𝟯. GenAI for Everyone:
https://deeplearning.ai/courses/generative-ai-for-everyone/
𝟭𝟰. AWS GenAI Foundation:
https://coursera.org/learn/generative-ai-with-llms
https://t.iss.one/CodeProgrammer
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🚀 16th AI by Hand ✍️ Workshops, Scholarships available 👉 https://lu.ma/te2q2zqu
Every Wednesday we’ve been bringing people together to learn AI by hand ✍️.
Next week marks our 16th workshop, and thanks to Google’s generous sponsorship, we’re offering scholarships for educators and students to join us.
Choose your session:
🙌 Deep Learning Beginner Math Workshop
Build the math foundation for deep learning:
1. Dot Product
2. Matrix Multiplication
3. Linear Layer
4. Activation
5. Artificial Neuron
🙌 🙌 Transformer in Excel Workshop (Intermediate)
For AI engineers who know how to use Transformers but want to open the black box. We’ll visualize every step—data flow, math, and dimension alignment—inside Excel.
🙌 🙌 🙌 Latest AI Paper Workshop (Advanced)
Work through a just-published model, architecture, or algorithm with brand-new AI by Hand exercises—crafted for this workshop only.
🙌 🙌 🙌 Deep Reinforcement Learning Workshop (Advanced)
From replay buffers to Monte Carlo, TD learning, Deep Q-Networks, and SARSA—understand value-based deep RL from the ground up.
📅 When: Every Wednesday
🎓 Scholarships: Available for educators & students (sponsored by Google)
Register 🔗 https://lu.ma/te2q2zqu
Every Wednesday we’ve been bringing people together to learn AI by hand ✍️.
Next week marks our 16th workshop, and thanks to Google’s generous sponsorship, we’re offering scholarships for educators and students to join us.
Choose your session:
🙌 Deep Learning Beginner Math Workshop
Build the math foundation for deep learning:
1. Dot Product
2. Matrix Multiplication
3. Linear Layer
4. Activation
5. Artificial Neuron
🙌 🙌 Transformer in Excel Workshop (Intermediate)
For AI engineers who know how to use Transformers but want to open the black box. We’ll visualize every step—data flow, math, and dimension alignment—inside Excel.
🙌 🙌 🙌 Latest AI Paper Workshop (Advanced)
Work through a just-published model, architecture, or algorithm with brand-new AI by Hand exercises—crafted for this workshop only.
🙌 🙌 🙌 Deep Reinforcement Learning Workshop (Advanced)
From replay buffers to Monte Carlo, TD learning, Deep Q-Networks, and SARSA—understand value-based deep RL from the ground up.
📅 When: Every Wednesday
🎓 Scholarships: Available for educators & students (sponsored by Google)
Register 🔗 https://lu.ma/te2q2zqu
❤9👍6
Top 150 Python Interview Questions
This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
🔸Link to PDF
👉 @DataScience4
This PDF covers the most frequently asked Python questions with answers to help you prepare for upcoming interviews.
🔸Link to PDF
👉 @DataScience4
❤3💯3
python-docx: Create and Modify Word Documents #python
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
Example
https://t.iss.one/DataScienceN 🚗
python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.
Installation
pip install python-docx
Example
from docx import Document
document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")
document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'
https://t.iss.one/DataScienceN 🚗
❤9🔥1
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Data scientists, this is for you — I dug up LeetCode for DS
DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here🍯
👉 https://t.iss.one/CodeProgrammer 👍
DataLemur — a powerful platform that collects real interview problems from Tesla, Facebook, Twitter, Microsoft, and other top companies
Inside: practical tasks on SQL, statistics, Python, and ML. You can filter by difficulty level and company
Top-notch for those preparing for interviews for Data Scientist / Data Analyst roles. Get it here
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Python library that adds Generative AI capabilities to Pandas!
PandasAI analyzes complex data frames and plot visualizations just by using natural language.
GitHub: https://github.com/sinaptik-ai/pandas-ai
https://t.iss.one/CodeProgrammer🖕
PandasAI analyzes complex data frames and plot visualizations just by using natural language.
GitHub: https://github.com/sinaptik-ai/pandas-ai
https://t.iss.one/CodeProgrammer
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❤8💯1
Forwarded from Data Science Jupyter Notebooks
🔥 Trending Repository: awesome-transformer-nlp
📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 41
🍴 Forks: 131 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: A curated list of NLP resources focused on Transformer networks, attention mechanism, GPT, BERT, ChatGPT, LLMs, and transfer learning.
🔗 Repository URL: https://github.com/cedrickchee/awesome-transformer-nlp
📖 Readme: https://github.com/cedrickchee/awesome-transformer-nlp#readme
📊 Statistics:
🌟 Stars: 1.1K stars
👀 Watchers: 41
🍴 Forks: 131 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #natural_language_processing #awesome #transformer #neural_networks #awesome_list #llama #transfer_learning #language_model #attention_mechanism #bert #gpt_2 #xlnet #pre_trained_language_models #gpt_3 #gpt_4 #chatgpt
==================================
🧠 By: https://t.iss.one/DataScienceM
❤2🔥1💯1
Forwarded from Data Science Jupyter Notebooks
🔥 Trending Repository: awesome-deeplearning-resources
📝 Description: Deep Learning and deep reinforcement learning research papers and some codes
🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
📊 Statistics:
🌟 Stars: 2.9K stars
👀 Watchers: 221
🍴 Forks: 666 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceN
📝 Description: Deep Learning and deep reinforcement learning research papers and some codes
🔗 Repository URL: https://github.com/endymecy/awesome-deeplearning-resources
📖 Readme: https://github.com/endymecy/awesome-deeplearning-resources#readme
📊 Statistics:
🌟 Stars: 2.9K stars
👀 Watchers: 221
🍴 Forks: 666 forks
💻 Programming Languages: Not available
🏷️ Related Topics:
#nlp #video #reinforcement_learning #deep_learning #neural_network #code #paper #corpus #modelzoo
==================================
🧠 By: https://t.iss.one/DataScienceN
❤2
Forwarded from Data Science Jupyter Notebooks
🔥 Trending Repository: awesome-deep-vision
📝 Description: A curated list of deep learning resources for computer vision
🔗 Repository URL: https://github.com/kjw0612/awesome-deep-vision
📖 Readme: https://github.com/kjw0612/awesome-deep-vision#readme
📊 Statistics:
🌟 Stars: 11K stars
👀 Watchers: 1.1k
🍴 Forks: 2.8K forks
💻 Programming Languages: Not available
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceN
📝 Description: A curated list of deep learning resources for computer vision
🔗 Repository URL: https://github.com/kjw0612/awesome-deep-vision
📖 Readme: https://github.com/kjw0612/awesome-deep-vision#readme
📊 Statistics:
🌟 Stars: 11K stars
👀 Watchers: 1.1k
🍴 Forks: 2.8K forks
💻 Programming Languages: Not available
🏷️ Related Topics: Not available
==================================
🧠 By: https://t.iss.one/DataScienceN
❤6
✨ Download a Free Python Cheat Sheet ✨
📖 Download a free Python 3 cheat sheet PDF put together by the Real Python team.
🏷️ #Python
📖 Download a free Python 3 cheat sheet PDF put together by the Real Python team.
🏷️ #Python
❤10💯1
Get top-tier market analysis: world events meet technical trading.
I'm Michael 👋. My team and I share our market insights daily on our Telegram channel. Over the past weekend, our strategies delivered up to +39% gains.
We will tell you everything on the channel, even for beginners.
Join the channel below! 👇
https://t.iss.one/+9mZure1faNRkOTE8
I'm Michael 👋. My team and I share our market insights daily on our Telegram channel. Over the past weekend, our strategies delivered up to +39% gains.
We will tell you everything on the channel, even for beginners.
Join the channel below! 👇
https://t.iss.one/+9mZure1faNRkOTE8
❤5
Let's Learn MCP with Python - Tutorial Series
Here's your comprehensive guide to understanding and building Model Context Protocol (MCP) Servers for Python developers through interactive learning experiences.
Repo: https://github.com/microsoft/lets-learn-mcp-python?tab=readme-ov-file
https://t.iss.one/CodeProgrammer🧠
Here's your comprehensive guide to understanding and building Model Context Protocol (MCP) Servers for Python developers through interactive learning experiences.
Repo: https://github.com/microsoft/lets-learn-mcp-python?tab=readme-ov-file
https://t.iss.one/CodeProgrammer
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❤5👍3🔥2
🔥 $10.000 WITH LISA!
Lisa earned $200,000 in a month, and now it’s YOUR TURN!
She’s made trading SO SIMPLE that anyone can do it.
❗️Just copy her signals every day
❗️Follow her trades step by step
❗️Earn $1,000+ in your first week – GUARANTEED!
🚨 BONUS: Lisa is giving away $10,000 to her subscribers!
Don’t miss this once-in-a-lifetime opportunity. Free access for the first 500 people only!
👉 CLICK HERE TO JOIN NOW 👈
Lisa earned $200,000 in a month, and now it’s YOUR TURN!
She’s made trading SO SIMPLE that anyone can do it.
❗️Just copy her signals every day
❗️Follow her trades step by step
❗️Earn $1,000+ in your first week – GUARANTEED!
🚨 BONUS: Lisa is giving away $10,000 to her subscribers!
Don’t miss this once-in-a-lifetime opportunity. Free access for the first 500 people only!
👉 CLICK HERE TO JOIN NOW 👈
❤1👎1
230+ Power BI Interview Questions.pdf
4.3 MB
30+ Power BI Interview Questions to Land Your Job!
Power BI mastery is key to landing your dream job, and understanding the right interview questions can make all the difference!
https://t.iss.one/CodeProgrammer 🔴
Power BI mastery is key to landing your dream job, and understanding the right interview questions can make all the difference!
https://t.iss.one/CodeProgrammer 🔴
❤6👍2💯2
One of the best free courses on LLMs is the Recent Advances on Foundation Models course from the University of Waterloo, and it has 21 lectures under these five sections:
1. Introduction to Foundation Models
2. Transformer Architecture
3. Large Language Models
4. (Large) Multimodal Models
5. Augmenting Foundation Models
➡️ Course link: https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/
https://t.iss.one/CodeProgrammer🔗
1. Introduction to Foundation Models
2. Transformer Architecture
3. Large Language Models
4. (Large) Multimodal Models
5. Augmenting Foundation Models
➡️ Course link: https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/
https://t.iss.one/CodeProgrammer
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