Best LLMs Courses
Link: https://www.mltut.com/best-large-language-models-courses/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer⭐️
Link: https://www.mltut.com/best-large-language-models-courses/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer
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👨🏻💻 The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data.
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└
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer
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Regression & Classification Loss Functions
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer⭐️
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer
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Python Network Programming Cheat Sheet 🖥
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer✅
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer
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The Big Book of Large Language Models by Damien Benveniste
✅ Chapters:
1⃣ Introduction
🔢 Language Models Before Transformers
🔢 Attention Is All You Need: The Original Transformer Architecture
🔢 A More Modern Approach To The Transformer Architecture
🔢 Multi-modal Large Language Models
🔢 Transformers Beyond Language Models
🔢 Non-Transformer Language Models
🔢 How LLMs Generate Text
🔢 From Words To Tokens
1⃣ 0⃣ Training LLMs to Follow Instructions
1⃣ 1⃣ Scaling Model Training
1⃣ 🔢 Fine-Tuning LLMs
1⃣ 🔢 Deploying LLMs
Read it: https://book.theaiedge.io/
Read it: https://book.theaiedge.io/
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
https://t.me/CodeProgrammer
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👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
https://t.me/CodeProgrammer
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10 Must-Know Python Libraries for LLMs in 2025
Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.
In this article, we explore 10 of the Python libraries every developer should know in 2025.
Read and learn:
https://machinelearningmastery.com/10-must-know-python-libraries-for-llms-in-2025/
https://t.me/CodeProgrammer✅
Large language models (LLMs) are changing the way we think about AI. They help with #chatbots, text generation, and search tools, among other natural language processing tasks and beyond. To work with #LLMs, you need the right #Python libraries.
In this article, we explore 10 of the Python libraries every developer should know in 2025.
Read and learn:
https://machinelearningmastery.com/10-must-know-python-libraries-for-llms-in-2025/
https://t.me/CodeProgrammer
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Anyone trying to deeply understand Large Language Models.
Checkout
by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource.
⭐️ Paper Link: arxiv.org/pdf/2501.09223
Checkout
Foundations of Large Language Models
by Tong Xiao & Jingbo Zhu. It’s one of the clearest, most comprehensive resource.
#LLMs #LargeLanguageModels #AIResearch #DeepLearning #MachineLearning #AIResources #NLP #AITheory #FoundationModels #AIUnderstanding
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Self-attention in LLMs, clearly explained
#SelfAttention #LLMs #Transformers #NLP #DeepLearning #MachineLearning #AIExplained #AttentionMechanism #AIConcepts #AIEducation
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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The 2025 MIT deep learning course is excellent, covering neural networks, CNNs, RNNs, and LLMs. You build three projects for hands-on experience as part of the course. It is entirely free. Highly recommended for beginners.
Enroll Free: https://introtodeeplearning.com/
Enroll Free: https://introtodeeplearning.com/
#DeepLearning #MITCourse #NeuralNetworks #CNN #RNN #LLMs #AIForBeginners #FreeCourse #MachineLearning #IntroToDeepLearning #AIProjects #LearnAI #AI2025
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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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.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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10 GitHub repos to build a career in AI engineering:
(100% free step-by-step roadmap)
1️⃣ ML for Beginners by Microsoft
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
2️⃣ AI for Beginners by Microsoft
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
3️⃣ Neural Networks: Zero to Hero
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
4️⃣ DL Paper Implementations
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
5️⃣ Made With ML
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
6️⃣ Hands-on LLMs
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
7️⃣ Advanced RAG Techniques
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
8️⃣ AI Agents for Beginners by Microsoft
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
9️⃣ Agents Towards Production
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
🔟 AI Engg. Hub
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
(100% free step-by-step roadmap)
A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.
Includes quizzes, lessons, and hands-on projects, with some videos.
GitHub repo → https://lnkd.in/dCxStbYv
This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.
Beginner-friendly, project-based, and full of real-world apps.
GitHub repo → https://lnkd.in/dwS5Jk9E
Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.
This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.
GitHub repo → https://lnkd.in/dXAQWucq
So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.
This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.
GitHub repo → https://lnkd.in/dTrtDrvs
Now it’s time to learn how to go from notebooks to production.
Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.
GitHub repo → https://lnkd.in/dYyjjBGb
- You've built neural nets.
- You've explored GPTs and LLMs.
Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.
GitHub repo → https://lnkd.in/dh2FwYFe
Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.
This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.
GitHub repo → https://lnkd.in/dBKxtX-D
After diving into LLMs and mastering RAG, learn how to build AI agents.
This hands-on course covers building AI agents using frameworks like AutoGen.
GitHub repo → https://lnkd.in/dbFeuznE
The above course will teach what AI agents are. Next, learn how to ship them.
This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.
GitHub repo → https://lnkd.in/dcwmamSb
To truly master LLMs, RAG, and AI agents, you need projects.
This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.
GitHub repo → https://lnkd.in/geMYm3b6
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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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.me/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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GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇
CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.
GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.
GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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