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Over the last year, several articles have been written to help candidates prepare for data science technical interviews. These resources cover a wide range of topics including machine learning, SQL, programming, statistics, and probability.
1️⃣ Machine Learning (ML) Interview
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
2️⃣ SQL Interview Preparation
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
3️⃣ Programming Questions
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
4️⃣ Statistics
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
5️⃣ Probability
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
🔜 All links are available in the GitHub repository:
https://lnkd.in/djcgcKRT
Types of ML Q&A in Data Science Interview
https://shorturl.at/syN37
ML Interview Q&A for Data Scientists
https://shorturl.at/HVWY0
Crack the ML Coding Q&A
https://shorturl.at/CDW08
Deep Learning Interview Q&A
https://shorturl.at/lHPZ6
Top LLMs Interview Q&A
https://shorturl.at/wGRSZ
Top CV Interview Q&A [Part 1]
https://rb.gy/51jcfi
Part 2
https://rb.gy/hqgkbg
Part 3
https://rb.gy/5z87be
13 SQL Statements for 90% of Data Science Tasks
https://rb.gy/dkdcl1
SQL Window Functions: Simplifying Complex Queries
https://t.ly/EwSlH
Ace the SQL Questions in the Technical Interview
https://lnkd.in/gNQbYMX9
Unlocking the Power of SQL: How to Ace Top N Problem Questions
https://lnkd.in/gvxVwb9n
How To Ace the SQL Ratio Problems
https://lnkd.in/g6JQqPNA
Cracking the SQL Window Function Coding Questions
https://lnkd.in/gk5u6hnE
SQL & Database Interview Q&A
https://lnkd.in/g75DsEfw
6 Free Resources for SQL Interview Preparation
https://lnkd.in/ghhiG79Q
Foundations of Data Structures [Part 1]
https://lnkd.in/gX_ZcmRq
Part 2
https://lnkd.in/gATY4rTT
Top Important Python Questions [Conceptual]
https://lnkd.in/gJKaNww5
Top Important Python Questions [Data Cleaning and Preprocessing]
https://lnkd.in/g-pZBs3A
Top Important Python Questions [Machine & Deep Learning]
https://lnkd.in/gZwcceWN
Python Interview Q&A
https://lnkd.in/gcaXc_JE
5 Python Tips for Acing DS Coding Interview
https://lnkd.in/gsj_Hddd
Mastering 5 Statistics Concepts to Boost Success
https://lnkd.in/gxEuHiG5
Mastering Hypothesis Testing for Interviews
https://lnkd.in/gSBbbmF8
Introduction to A/B Testing
https://lnkd.in/g35Jihw6
Statistics Interview Q&A for Data Scientists
https://lnkd.in/geHCCt6Q
15 Probability Concepts to Review [Part 1]
https://lnkd.in/g2rK2tQk
Part 2
https://lnkd.in/gQhXnKwJ
Probability Interview Q&A [Conceptual Questions]
https://lnkd.in/g5jyKqsp
Probability Interview Q&A [Mathematical Questions]
https://lnkd.in/gcWvPhVj
https://lnkd.in/djcgcKRT
#DataScience #InterviewPrep #MachineLearning #SQL #Python #Statistics #Probability #CodingInterview #AIBootcamp #DeepLearning #LLMs #ComputerVision #GitHubResources #CareerInDataScience
✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Python | Machine Learning | Coding | R
Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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What is torch.nn really?
This article explains it quite well.
📌 Read
✉️ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well.
📌 Read
#pytorch #AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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🔥 Trending Repository: awesome-llm-apps
📝 Description: Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
🔗 Repository URL: https://github.com/Shubhamsaboo/awesome-llm-apps
🌐 Website: https://www.theunwindai.com
📖 Readme: https://github.com/Shubhamsaboo/awesome-llm-apps#readme
📊 Statistics:
🌟 Stars: 58.1K stars
👀 Watchers: 664
🍴 Forks: 6.9K forks
💻 Programming Languages: Python - JavaScript - TypeScript - CSS - PLpgSQL - HTML
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
🔗 Repository URL: https://github.com/Shubhamsaboo/awesome-llm-apps
🌐 Website: https://www.theunwindai.com
📖 Readme: https://github.com/Shubhamsaboo/awesome-llm-apps#readme
📊 Statistics:
🌟 Stars: 58.1K stars
👀 Watchers: 664
🍴 Forks: 6.9K forks
💻 Programming Languages: Python - JavaScript - TypeScript - CSS - PLpgSQL - HTML
🏷️ Related Topics:
#python #rag #llms
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: dyad
📝 Description: Free, local, open-source AI app builder ✨ v0 / lovable / Bolt alternative 🌟 Star if you like it!
🔗 Repository URL: https://github.com/dyad-sh/dyad
🌐 Website: https://dyad.sh
📖 Readme: https://github.com/dyad-sh/dyad#readme
📊 Statistics:
🌟 Stars: 13.2K stars
👀 Watchers: 73
🍴 Forks: 1.3K forks
💻 Programming Languages: TypeScript - JavaScript - CSS - HTML - Shell - Python
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Free, local, open-source AI app builder ✨ v0 / lovable / Bolt alternative 🌟 Star if you like it!
🔗 Repository URL: https://github.com/dyad-sh/dyad
🌐 Website: https://dyad.sh
📖 Readme: https://github.com/dyad-sh/dyad#readme
📊 Statistics:
🌟 Stars: 13.2K stars
👀 Watchers: 73
🍴 Forks: 1.3K forks
💻 Programming Languages: TypeScript - JavaScript - CSS - HTML - Shell - Python
🏷️ Related Topics:
#github #react #typescript #nextjs #artificial_intelligence #gemini #openai #bolt #v0 #lovable #vercel #llm #llms #generative_ai #anthropic #ollama #qwen #deepseek #ai_app_builder
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: generative-ai-for-beginners
📝 Description: 21 Lessons, Get Started Building with Generative AI
🔗 Repository URL: https://github.com/microsoft/generative-ai-for-beginners
📖 Readme: https://github.com/microsoft/generative-ai-for-beginners#readme
📊 Statistics:
🌟 Stars: 95.7K stars
👀 Watchers: 827
🍴 Forks: 50.1K forks
💻 Programming Languages: Jupyter Notebook - Python - JavaScript - TypeScript - Shell - PowerShell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: 21 Lessons, Get Started Building with Generative AI
🔗 Repository URL: https://github.com/microsoft/generative-ai-for-beginners
📖 Readme: https://github.com/microsoft/generative-ai-for-beginners#readme
📊 Statistics:
🌟 Stars: 95.7K stars
👀 Watchers: 827
🍴 Forks: 50.1K forks
💻 Programming Languages: Jupyter Notebook - Python - JavaScript - TypeScript - Shell - PowerShell
🏷️ Related Topics:
#ai #azure #transformers #openai #gpt #language_model #semantic_search #dall_e #prompt_engineering #llms #generative_ai #generativeai #chatgpt #microsoft_for_beginners
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: Hands-On-Large-Language-Models
📝 Description: Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
🔗 Repository URL: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
🌐 Website: https://www.llm-book.com/
📖 Readme: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models#readme
📊 Statistics:
🌟 Stars: 14.5K stars
👀 Watchers: 154
🍴 Forks: 3.4K forks
💻 Programming Languages: Jupyter Notebook
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
🔗 Repository URL: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
🌐 Website: https://www.llm-book.com/
📖 Readme: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models#readme
📊 Statistics:
🌟 Stars: 14.5K stars
👀 Watchers: 154
🍴 Forks: 3.4K forks
💻 Programming Languages: Jupyter Notebook
🏷️ Related Topics:
#book #artificial_intelligence #oreilly #oreilly_books #large_language_models #llm #llms
==================================
🧠 By: https://t.iss.one/DataScienceM
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🔥 Trending Repository: genai-toolbox
📝 Description: MCP Toolbox for Databases is an open source MCP server for databases.
🔗 Repository URL: https://github.com/googleapis/genai-toolbox
🌐 Website: https://googleapis.github.io/genai-toolbox/getting-started/introduction/
📖 Readme: https://github.com/googleapis/genai-toolbox#readme
📊 Statistics:
🌟 Stars: 9.8K stars
👀 Watchers: 61
🍴 Forks: 749 forks
💻 Programming Languages: Go - JavaScript - CSS - HTML - Shell - Dockerfile
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: MCP Toolbox for Databases is an open source MCP server for databases.
🔗 Repository URL: https://github.com/googleapis/genai-toolbox
🌐 Website: https://googleapis.github.io/genai-toolbox/getting-started/introduction/
📖 Readme: https://github.com/googleapis/genai-toolbox#readme
📊 Statistics:
🌟 Stars: 9.8K stars
👀 Watchers: 61
🍴 Forks: 749 forks
💻 Programming Languages: Go - JavaScript - CSS - HTML - Shell - Dockerfile
🏷️ Related Topics:
#mcp #databases #llms #genai
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: ART
📝 Description: Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen2.5, Qwen3, Llama, and more!
🔗 Repository URL: https://github.com/OpenPipe/ART
🌐 Website: https://art.openpipe.ai
📖 Readme: https://github.com/OpenPipe/ART#readme
📊 Statistics:
🌟 Stars: 6K stars
👀 Watchers: 35
🍴 Forks: 380 forks
💻 Programming Languages: Python - Jupyter Notebook - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen2.5, Qwen3, Llama, and more!
🔗 Repository URL: https://github.com/OpenPipe/ART
🌐 Website: https://art.openpipe.ai
📖 Readme: https://github.com/OpenPipe/ART#readme
📊 Statistics:
🌟 Stars: 6K stars
👀 Watchers: 35
🍴 Forks: 380 forks
💻 Programming Languages: Python - Jupyter Notebook - Shell
🏷️ Related Topics:
#agent #reinforcement_learning #rl #lora #llms #qwen #kimi_ai #agentic_ai #grpo #qwen3
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: chroma
📝 Description: Open-source search and retrieval database for AI applications.
🔗 Repository URL: https://github.com/chroma-core/chroma
🌐 Website: https://www.trychroma.com/
📖 Readme: https://github.com/chroma-core/chroma#readme
📊 Statistics:
🌟 Stars: 22.2K stars
👀 Watchers: 121
🍴 Forks: 1.8K forks
💻 Programming Languages: Rust - Python - TypeScript - Go - Jupyter Notebook - JavaScript
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Open-source search and retrieval database for AI applications.
🔗 Repository URL: https://github.com/chroma-core/chroma
🌐 Website: https://www.trychroma.com/
📖 Readme: https://github.com/chroma-core/chroma#readme
📊 Statistics:
🌟 Stars: 22.2K stars
👀 Watchers: 121
🍴 Forks: 1.8K forks
💻 Programming Languages: Rust - Python - TypeScript - Go - Jupyter Notebook - JavaScript
🏷️ Related Topics:
#rust #database #ai #embeddings #rust_lang #document_retrieval #rag #vector_database #llm #llms
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: humanlayer
📝 Description: HumanLayer enables AI agents to communicate with humans in tool-based and async workflows. Guarantee human oversight of high-stakes function calls with approval workflows across slack, email and more. Bring your LLM and Framework of choice and start giving your AI agents safe access to the world. Agentic Workflows, human in the loop, tool calling
🔗 Repository URL: https://github.com/humanlayer/humanlayer
🌐 Website: https://humanlayer.dev
📖 Readme: https://github.com/humanlayer/humanlayer#readme
📊 Statistics:
🌟 Stars: 1.3K stars
👀 Watchers: 8
🍴 Forks: 136 forks
💻 Programming Languages: TypeScript - Go - Python - CSS - Makefile - Shell
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: HumanLayer enables AI agents to communicate with humans in tool-based and async workflows. Guarantee human oversight of high-stakes function calls with approval workflows across slack, email and more. Bring your LLM and Framework of choice and start giving your AI agents safe access to the world. Agentic Workflows, human in the loop, tool calling
🔗 Repository URL: https://github.com/humanlayer/humanlayer
🌐 Website: https://humanlayer.dev
📖 Readme: https://github.com/humanlayer/humanlayer#readme
📊 Statistics:
🌟 Stars: 1.3K stars
👀 Watchers: 8
🍴 Forks: 136 forks
💻 Programming Languages: TypeScript - Go - Python - CSS - Makefile - Shell
🏷️ Related Topics:
#ai #approval_process #agents #human_in_the_loop #escalation_policy #llm #llms #function_calling #agentic_workflow #agentic_ai #humanlayer #human_as_tool
==================================
🧠 By: https://t.iss.one/DataScienceM
🔥 Trending Repository: transformerlab-app
📝 Description: Open Source Application for Advanced LLM + Diffusion Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
🔗 Repository URL: https://github.com/transformerlab/transformerlab-app
🌐 Website: https://transformerlab.ai/
📖 Readme: https://github.com/transformerlab/transformerlab-app#readme
📊 Statistics:
🌟 Stars: 3.9K stars
👀 Watchers: 31
🍴 Forks: 363 forks
💻 Programming Languages: TypeScript - JavaScript
🏷️ Related Topics:
==================================
🧠 By: https://t.iss.one/DataScienceM
📝 Description: Open Source Application for Advanced LLM + Diffusion Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
🔗 Repository URL: https://github.com/transformerlab/transformerlab-app
🌐 Website: https://transformerlab.ai/
📖 Readme: https://github.com/transformerlab/transformerlab-app#readme
📊 Statistics:
🌟 Stars: 3.9K stars
👀 Watchers: 31
🍴 Forks: 363 forks
💻 Programming Languages: TypeScript - JavaScript
🏷️ Related Topics:
#electron #transformers #llama #lora #diffusion #mlx #diffusion_models #llms #stability_diffusion #rlhf
==================================
🧠 By: https://t.iss.one/DataScienceM
🤖🧠 How oLLM Makes Large-Context AI Models Run Smoothly on 8GB GPUs
🗓️ 11 Oct 2025
📚 AI News & Trends
Artificial intelligence has revolutionized the way we process information, analyze data, and automate complex tasks. With the rise of large language models (LLMs), AI capabilities have grown exponentially, enabling applications from natural language understanding to multimodal reasoning. However, running these models efficiently especially with massive context windows, remains a challenge due to their high memory ...
#oLLM #LargeContextAI #AIGPU #MachineLearning #LLMs #AIOptimization
🗓️ 11 Oct 2025
📚 AI News & Trends
Artificial intelligence has revolutionized the way we process information, analyze data, and automate complex tasks. With the rise of large language models (LLMs), AI capabilities have grown exponentially, enabling applications from natural language understanding to multimodal reasoning. However, running these models efficiently especially with massive context windows, remains a challenge due to their high memory ...
#oLLM #LargeContextAI #AIGPU #MachineLearning #LLMs #AIOptimization
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