Machine Learning Specialization โ Study Notes & Labs ๐ ๐ฌ
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.๐งโ๐
๐ Repo: https://github.com/TruongDat05/machine-learning-notes-and-code
https://t.me/CodeProgrammer๐
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng.
https://t.me/CodeProgrammer
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๐ Thesis โข Dissertation โข Research โข Programming โข Simulation
From a single research ideaโฆ
to a complete academic masterpiece.
๐น Professional assistance for:
โ๏ธ Masterโs & PhD Theses
โ๏ธ ISI / Scopus Articles
โ๏ธ Research Proposals & Methodology
โ๏ธ Data Analysis & Statistical Modeling
โ๏ธ AI & Machine Learning Projects
โ๏ธ MATLAB โข Python โข Simulink โข Abaqus โข COMSOL โข Ansys โข ETAP โข PSCAD โข HOMER โข Proteus โข LabVIEW
โ๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โ๏ธ Rare & High-Quality Datasets
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โจ Turning complex ideas into publishable research.
๐ฉ Contact us for consultation and project evaluation.
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From a single research ideaโฆ
to a complete academic masterpiece.
๐น Professional assistance for:
โ๏ธ Masterโs & PhD Theses
โ๏ธ ISI / Scopus Articles
โ๏ธ Research Proposals & Methodology
โ๏ธ Data Analysis & Statistical Modeling
โ๏ธ AI & Machine Learning Projects
โ๏ธ MATLAB โข Python โข Simulink โข Abaqus โข COMSOL โข Ansys โข ETAP โข PSCAD โข HOMER โข Proteus โข LabVIEW
โ๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โ๏ธ Rare & High-Quality Datasets
โ๏ธ Simulation Projects & Optimization Algorithms
โ๏ธ Academic Presentation Design
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๐ Accurate Results
๐ Professional Documentation
๐ป Clean & Structured Coding
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Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
โจ Turning complex ideas into publishable research.
๐ฉ Contact us for consultation and project evaluation.
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๐ AI / ML / DL Learning Resources Hub ๐ค๐ง
A structured, end-to-end roadmap to master AI โ from fundamentals to cutting-edge research. ๐
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals ๐ฉโ๐ป๐จโ๐ป who want to build strong foundations and progress confidently from beginner to advanced levels ๐. This hub brings together the high-quality books ๐, courses ๐, playlists ๐ต, research papers ๐, tools ๐ , and learning roadmaps ๐บ covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science ๐, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps ๐, all organized in a clear, practical, and industry-relevant manner.
The resources are selected to balance theory ๐ง , intuition ๐ก, and real-world application ๐, allowing learners to follow modules sequentially or in parallel โณ based on their goals.
โญ๏ธ Recommended resources highlight high-impact content widely used in academia ๐, research ๐ฌ, and industry ๐ญ, ensuring you focus on what truly matters in modern AI.
๐ Repo: https://github.com/bishwaghimire/ai-learning-roadmaps
By: https://t.me/CodeProgrammer
A structured, end-to-end roadmap to master AI โ from fundamentals to cutting-edge research. ๐
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals ๐ฉโ๐ป๐จโ๐ป who want to build strong foundations and progress confidently from beginner to advanced levels ๐. This hub brings together the high-quality books ๐, courses ๐, playlists ๐ต, research papers ๐, tools ๐ , and learning roadmaps ๐บ covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science ๐, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps ๐, all organized in a clear, practical, and industry-relevant manner.
The resources are selected to balance theory ๐ง , intuition ๐ก, and real-world application ๐, allowing learners to follow modules sequentially or in parallel โณ based on their goals.
โญ๏ธ Recommended resources highlight high-impact content widely used in academia ๐, research ๐ฌ, and industry ๐ญ, ensuring you focus on what truly matters in modern AI.
By: https://t.me/CodeProgrammer
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๐ Thesis โข Dissertation โข Research โข Programming โข Simulation
From a single research ideaโฆ
to a complete academic masterpiece.
๐น Professional assistance for:
โ๏ธ Masterโs & PhD Theses
โ๏ธ ISI / Scopus Articles
โ๏ธ Research Proposals & Methodology
โ๏ธ Data Analysis & Statistical Modeling
โ๏ธ AI & Machine Learning Projects
โ๏ธ MATLAB โข Python โข Simulink โข Abaqus โข COMSOL โข Ansys โข ETAP โข PSCAD โข HOMER โข Proteus โข LabVIEW
โ๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โ๏ธ Rare & High-Quality Datasets
โ๏ธ Simulation Projects & Optimization Algorithms
โ๏ธ Academic Presentation Design
โ๏ธ Journal Revision & Reviewer Response Preparation
๐ Accurate Results
๐ Professional Documentation
๐ป Clean & Structured Coding
๐ Full Confidentiality
โณ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
โจ Turning complex ideas into publishable research.
๐ฉ Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
From a single research ideaโฆ
to a complete academic masterpiece.
๐น Professional assistance for:
โ๏ธ Masterโs & PhD Theses
โ๏ธ ISI / Scopus Articles
โ๏ธ Research Proposals & Methodology
โ๏ธ Data Analysis & Statistical Modeling
โ๏ธ AI & Machine Learning Projects
โ๏ธ MATLAB โข Python โข Simulink โข Abaqus โข COMSOL โข Ansys โข ETAP โข PSCAD โข HOMER โข Proteus โข LabVIEW
โ๏ธ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
โ๏ธ Rare & High-Quality Datasets
โ๏ธ Simulation Projects & Optimization Algorithms
โ๏ธ Academic Presentation Design
โ๏ธ Journal Revision & Reviewer Response Preparation
๐ Accurate Results
๐ Professional Documentation
๐ป Clean & Structured Coding
๐ Full Confidentiality
โณ On-Time Delivery
Your research deserves more than copy-paste work.
It deserves precision, originality, and engineering-level thinking.
โจ Turning complex ideas into publishable research.
๐ฉ Contact us for consultation and project evaluation.
https://t.me/Omidyzd62
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Automate research with NotebookLM + Python ๐ค๐
Notebooklm-py โ is a unofficial library for working with Google NotebookLM,๐๐ง which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools โ works both via Python and CLI โจ๏ธ
What it can do:
โข integration with AI agents and Claude Code ๐ค
โข automatic import and processing of sources ๐ฅ
โข generation of podcasts, videos, and educational materials ๐๏ธ๐ฅ๐
โข working via Python API and command line ๐ป
โข using unofficial Google APIs ๐ง
https://github.com/teng-lin/notebooklm-py
https://t.me/CodeProgrammer๐ฑ
Notebooklm-py โ is a unofficial library for working with Google NotebookLM,๐๐ง which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools โ works both via Python and CLI โจ๏ธ
What it can do:
โข integration with AI agents and Claude Code ๐ค
โข automatic import and processing of sources ๐ฅ
โข generation of podcasts, videos, and educational materials ๐๏ธ๐ฅ๐
โข working via Python API and command line ๐ป
โข using unofficial Google APIs ๐ง
https://github.com/teng-lin/notebooklm-py
https://t.me/CodeProgrammer
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Forwarded from Machine Learning
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Google Gemma 4's pre-training is completely free
All you need is a browser and access to more than 500 models to choose from.
The process is simple:
1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin
Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
It's done๐
๐ https://t.me/MachineLearning9
All you need is a browser and access to more than 500 models to choose from.
The process is simple:
1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin
Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
It's done
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Forwarded from Python Courses & Resources
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14 minutes with an Anthropic engineer will teach you more about building agents ๐ค than most devs figure out in months of trial and error ๐ .
Same guy who wrote โBuilding Effective Agentsโ, the post every AI builder has bookmarked ๐.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production ๐:
โ When to use workflows vs. agents (most people get this wrong) โ
โ Why simple > clever, every single time โ
โ The orchestrator-worker pattern that scales ๐
โ When NOT to build an agent at all ๐
If youโre shipping AI products in 2026 and havenโt watched this, youโre doing it on hard mode ๐ฎ.
14 minutes. Bookmark it ๐. Watch it twice ๐.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
Same guy who wrote โBuilding Effective Agentsโ, the post every AI builder has bookmarked ๐.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production ๐:
โ When to use workflows vs. agents (most people get this wrong) โ
โ Why simple > clever, every single time โ
โ The orchestrator-worker pattern that scales ๐
โ When NOT to build an agent at all ๐
If youโre shipping AI products in 2026 and havenโt watched this, youโre doing it on hard mode ๐ฎ.
14 minutes. Bookmark it ๐. Watch it twice ๐.
#AI #Agents #Tech #DevCommunity #FutureTech #ProgrammingConcepts
โค10๐3๐2
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๐ Interactive textbook on probability theory and statistics ๐โจ
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. ๐๐ฒ
No tons of formulas and boring theory โ everything is demonstrated through interactive examples and simulations. ๐ป๐ฌ
โ๏ธ Download here ๐
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.me/CodeProgrammer
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. ๐๐ฒ
No tons of formulas and boring theory โ everything is demonstrated through interactive examples and simulations. ๐ป๐ฌ
โ๏ธ Download here ๐
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.me/CodeProgrammer
โค8
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Most people donโt fail because they lack ambition ๐ฏ
They fail because their time and energy leak into distractions before they ever reach the goal ๐ฐ๐
This visual explains it perfectly: ๐
Time + Energy are only useful when filtered through discipline ๐ง ๐ก
Without discipline, distractions absorb everything: ๐
โข endless notifications ๐ฑ
โข reactive meetings ๐ค
โข poor sleep ๐ด
โข stress-driven habits ๐คฏ
โข multitasking disguised as productivity ๐
And in high-performance environments, this becomes a leadership issue, not just a personal one ๐ข๐
I see this often in corporate wellness workshops and executive coaching sessions. ๐ฃ
Leaders want better focus, resilience, and performance โก๏ธ
But the real challenge is not motivation ๐ซ๐ฅ
Itโs protecting their cognitive energy daily ๐ง ๐
Discipline is not punishment โ๏ธ
Itโs a system that helps you direct your energy intentionally ๐ฏ
Sometimes that means: โ๏ธ
โ๏ธ starting the day without your phone ๐ต
โ๏ธ eating to stabilize energy and focus ๐ฅ
โ๏ธ creating recovery moments between meetings โธ๏ธ
โ๏ธ reducing unnecessary inputs ๐
โ๏ธ building routines that lower decision fatigue ๐ง
Because peak performance is rarely about doing more ๐
Itโs about allowing less distraction to consume what matters most ๐ฏโจ
Your goals are not only built by effort ๐ช
They are built by what you consistently refuse to give your energy to ๐ซ๐ธ
And if youโre ready to start, I created something simple for you: ๐
My 7 Days to Peak Performance email series ๐ง
designed to help you improve your energy, focus, and productivity with practical daily strategies you can actually stick to. ๐ โ
You can join here: ๐
https://lnkd.in/eA3h9wb8
#PeakPerformance #ProductivityHacks #FocusMastery #Discipline #ExecutiveCoaching #MindsetShift
They fail because their time and energy leak into distractions before they ever reach the goal ๐ฐ๐
This visual explains it perfectly: ๐
Time + Energy are only useful when filtered through discipline ๐ง ๐ก
Without discipline, distractions absorb everything: ๐
โข endless notifications ๐ฑ
โข reactive meetings ๐ค
โข poor sleep ๐ด
โข stress-driven habits ๐คฏ
โข multitasking disguised as productivity ๐
And in high-performance environments, this becomes a leadership issue, not just a personal one ๐ข๐
I see this often in corporate wellness workshops and executive coaching sessions. ๐ฃ
Leaders want better focus, resilience, and performance โก๏ธ
But the real challenge is not motivation ๐ซ๐ฅ
Itโs protecting their cognitive energy daily ๐ง ๐
Discipline is not punishment โ๏ธ
Itโs a system that helps you direct your energy intentionally ๐ฏ
Sometimes that means: โ๏ธ
โ๏ธ starting the day without your phone ๐ต
โ๏ธ eating to stabilize energy and focus ๐ฅ
โ๏ธ creating recovery moments between meetings โธ๏ธ
โ๏ธ reducing unnecessary inputs ๐
โ๏ธ building routines that lower decision fatigue ๐ง
Because peak performance is rarely about doing more ๐
Itโs about allowing less distraction to consume what matters most ๐ฏโจ
Your goals are not only built by effort ๐ช
They are built by what you consistently refuse to give your energy to ๐ซ๐ธ
And if youโre ready to start, I created something simple for you: ๐
My 7 Days to Peak Performance email series ๐ง
designed to help you improve your energy, focus, and productivity with practical daily strategies you can actually stick to. ๐ โ
You can join here: ๐
https://lnkd.in/eA3h9wb8
#PeakPerformance #ProductivityHacks #FocusMastery #Discipline #ExecutiveCoaching #MindsetShift
โค10๐7๐2
๐๐ธ 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! ๐๐ธ
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Join our channel today for free! Tomorrow it will cost 500$!
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You can join at this link! ๐๐
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๐ Demystifying Activation Functions! ๐ง โจ
Ever wondered why activation functions are so critical in neural networks? ๐ค๐ค
Theyโre the secret sauce that allows models to capture complex, nonlinear relationships! ๐ฅ๐
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ๐๐
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB ๐๐
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
Ever wondered why activation functions are so critical in neural networks? ๐ค๐ค
Theyโre the secret sauce that allows models to capture complex, nonlinear relationships! ๐ฅ๐
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ๐๐
Learn more in super-detailed guide: https://lnkd.in/e4CydTtB ๐๐
#NeuralNetworks #DeepLearning #ActivationFunctions #Python #NumPy #AI
โค6๐ฅ2๐1
reader3 ๐โจ
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. ๐ฉ๐ป
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. โณ๐ซ
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ๐๐ ๏ธ It's a lightweight EPUB reader that allows you to read a book together with AI. ๐ค๐
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ๐๐งญ You can also manage your library through folders. ๐โจ
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ๐๐
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ๐๐ It significantly improves the reading experience when paired with AI. ๐๐ง
And it's very easy to get started - just run two commands via uv. โก๐ ๏ธ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ๐๐ค๐ค
๐ Language: #Python 61.0%
โญ๏ธ Stars: 1.5k
โก๏ธ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammerโ
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. ๐ฉ๐ป
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. โณ๐ซ
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ๐๐ ๏ธ It's a lightweight EPUB reader that allows you to read a book together with AI. ๐ค๐
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ๐๐งญ You can also manage your library through folders. ๐โจ
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ๐๐
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ๐๐ It significantly improves the reading experience when paired with AI. ๐๐ง
And it's very easy to get started - just run two commands via uv. โก๐ ๏ธ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ๐๐ค๐ค
๐ Language: #Python 61.0%
โญ๏ธ Stars: 1.5k
โก๏ธ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammer
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Forwarded from Learn Python Coding
Cheat sheet on the basics of Python: ๐๐
basic syntax and language rules ๐
scalar types โ basic data types (int, float, bool, str, NoneType) ๐ข
datetime โ working with date and time ๐ โฐ
data structures โ Python data structures (list, tuple, dict, set) ๐
list โ mutable lists for storing data collections ๐
tuple โ immutable sequences of values ๐
dict (hash map) โ storing data in a key-value format ๐
set โ unique elements without order ๐
slicing โ obtaining parts of sequences through indices and step โ๏ธ
module/library โ connecting modules and libraries ๐
help functions โ using help() and dir() to explore the Python API ๐
#Python #Coding #DataScience #Programming #Tech #DevCommunity
basic syntax and language rules ๐
scalar types โ basic data types (int, float, bool, str, NoneType) ๐ข
datetime โ working with date and time ๐ โฐ
data structures โ Python data structures (list, tuple, dict, set) ๐
list โ mutable lists for storing data collections ๐
tuple โ immutable sequences of values ๐
dict (hash map) โ storing data in a key-value format ๐
set โ unique elements without order ๐
slicing โ obtaining parts of sequences through indices and step โ๏ธ
module/library โ connecting modules and libraries ๐
help functions โ using help() and dir() to explore the Python API ๐
#Python #Coding #DataScience #Programming #Tech #DevCommunity
โค2๐2
Forwarded from Machine Learning
๐ฃ Rust Interview Deep Dive ๐ฆ๐
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
A repository for systematic preparation for Rust interviews at the middle, senior, and staff levels. ๐ผ๐
Inside 100 real questions from interviews in product and infrastructure companies, detailed analyses with code examples and scenarios of tasks that occur in production. ๐ป๐๏ธ Not "guess the program's output", but the mechanics on which real services are built. ๐ ๏ธ๐
Here are lock-free structures, self-referential types in async, FFI with tensor libraries, correct Send on guards via await, memory ordering under loom, soundness of custom collections. ๐โก And it all starts with the basics. Ownership, borrowing, lifetimes. ๐งฑ๐ Those who want can start from scratch or at the staff level. ๐ถโโ๏ธ๐จโ๐ป
https://github.com/Develp10/rustinterviewquiestions ๐
#Rust #Programming #InterviewPrep #SoftwareEngineering #SystemsProgramming #CareerGrowth
GitHub
GitHub - Develp10/rustinterviewquiestions: Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน
Rust ะฒะพะฟะพััั ั ัะพะฑะตัะตะดะพะฒะฐะฝะธะน . Contribute to Develp10/rustinterviewquiestions development by creating an account on GitHub.
2โค5๐1
AI is moving fast. Accountability is not.
That is why we built the open source core of Forkit Dev.
Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle.
Open source repo:
https://github.com/arpitasarker01/Forkit_Dev
If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.
That is why we built the open source core of Forkit Dev.
Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle.
Open source repo:
https://github.com/arpitasarker01/Forkit_Dev
If you care about trustworthy AI, open source infrastructure, model lineage, or compliance ready deployment, check it out and share your thoughts.
GitHub
GitHub - Forkit-Dev-Core/Forkit_Dev: Forkit Core is an open source passport layer for AI models and agents with GitHub CI validationโฆ
Forkit Core is an open source passport layer for AI models and agents with GitHub CI validation, local verification, and Hugging Face-compatible export. - Forkit-Dev-Core/Forkit_Dev
1๐3โค1
Machine Learning with Python pinned ยซAI is moving fast. Accountability is not. That is why we built the open source core of Forkit Dev. Forkit Dev introduces Model Passports and Agent Passports so AI systems can be tracked, verified, and understood across their lifecycle. Open source repo:โฆยป
Forwarded from Machine Learning
๐ Master Binary Classification with Neural Networks! ๐ง โจ
Ever wondered how to build a neural network from scratch in Python using NumPy? ๐๐
Binary classification is at the heart of many machine learning applications. ๐ฏ๐ค
Our super-detailed guide walks you through the entire process step by step. ๐๐
๐ก Dive in and start building your own neural network today! ๐๐ฅ
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? ๐๐
Binary classification is at the heart of many machine learning applications. ๐ฏ๐ค
Our super-detailed guide walks you through the entire process step by step. ๐๐
๐ก Dive in and start building your own neural network today! ๐๐ฅ
https://tinztwinshub.com/data-science/a-beginners-guide-to-developing-an-artificial-neural-network-from-zero/
#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
โค7
"Dive into Deep Learning" ๐๐ค is an open-source book that forms the mathematical foundation for large language models. ๐ง ๐
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
It covers linear algebra, mathematical analysis, probability theory, optimization methods, backpropagation, attention mechanisms, and transformer architectures. ๐งฎ๐๐
The book progressively moves from classical neural networks and convolutional neural networks to modern transformers and practical techniques used in large language models. ๐๐๐ง
It contains over 1,000 pages ๐ and provides clear explanations, practical examples, and exercises. โ ๐ Making it one of the most comprehensive free resources for understanding the mathematical structure of modern artificial intelligence systems and language models. ๐๐๐ค
arxiv.org/pdf/2106.11342 ๐
#DeepLearning #AI #MachineLearning #NeuralNetworks #Transformers #OpenSource
โค9๐3๐1
Forwarded from Machine Learning
๐ฅ Awesome open-source project to learn more about Transformer Models! ๐คโจ
We found this interactive website that shows you visually how transformer models work. ๐๐
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
We found this interactive website that shows you visually how transformer models work. ๐๐
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
#TransformerModels #OpenSource #AI #MachineLearning #DataScience #Tech
โค5๐1