๐ฅ Free IT & Dev Study Kits โ 2026 Prep, No Signup
If youโre prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.
๐ Cert E-Book โ covers Cisco & AWS fundamentals, good for first-time cert takers
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๐ช Online Free Course โ structured path, works as a refresher before exams
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โ๏ธ Free AI Materials โ LLM basics to fine-tuning practice notebooks
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๐ง Free Mock Exam โ test yourself before booking the real thing
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๐ Save this or forward it to your dev/study group โ good resources are worth sharing. ๐ช
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๐ฒ Personalized help: https://wa.link/xd2fu6
#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers
If youโre prepping for cloud, dev, or AI roles this year, these free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams. No signup traps, no hidden fees.
๐ Cert E-Book โ covers Cisco & AWS fundamentals, good for first-time cert takers
โ https://bit.ly/4yrk1Ti
๐ช Online Free Course โ structured path, works as a refresher before exams
โ https://bit.ly/4hUprRP
โ๏ธ Free AI Materials โ LLM basics to fine-tuning practice notebooks
โ https://bit.ly/4yt1iXy
๐ Cloud Study Guide โ cheat-sheet style, handy for interview prep
โ https://bit.ly/4el4Eod
๐ง Free Mock Exam โ test yourself before booking the real thing
โ https://bit.ly/4sbWlkl
๐ Save this or forward it to your dev/study group โ good resources are worth sharing. ๐ช
๐ Community: https://chat.whatsapp.com/CS7VOvQCLZG5BJPazBGm1v
๐ฒ Personalized help: https://wa.link/xd2fu6
#Python #Cloud #AWS #Cisco #AI #MachineLearning #DevOps #ITCertification #FreeResources #TechCareers
โค3
I thought I understood how GPU loading works until I started writing this guide. ๐ค
I was constantly confusing three different concepts:
โข The amount of work we are launching.
โข The amount of work that is actually placed on the GPU.
โข The amount of work that is ready to be executed right now.
But these are completely different things. โ ๏ธ
Consider a CUDA kernel with 240 blocks, each containing 256 threads. This results in 61,440 logical threads, or 1920 warps. However, the number of launched threads doesn't tell you how many warps can actually be present on the GPU simultaneously. ๐งฎ
Blocks are placed on streaming multiprocessors (SMs) based on the available registers, shared memory, and limitations on the number of threads, warps, and blocks.
Even after being placed on an SM, a warp can be waiting for a memory operation to complete, a dependency between arithmetic operations to be resolved, or for synchronization. The scheduler can only select warps that are actually ready to issue instructions for execution. โณ
This changed my approach to analyzing performance metrics. ๐
Occupancy is a measure of the ratio of the number of active warps on an SM to the maximum number of warps that the SM can support.
GPU utilization measures something different. In NVML, this metric reflects the percentage of the measurement interval during which at least one CUDA kernel was running.
Therefore, the GPU can show 100% utilization even if the Tensor Cores are not being used to their full potential, the HBM bandwidth is not saturated, and performance is limited by one of the system components. At the same time, a significant portion of the GPU's resources may remain underutilized. ๐
Similar misconceptions arise with memory. Registers, shared memory, L1, L2, and HBM are not simply levels of memory that differ in speed. Each has its own areas of accessibility, capacity, management characteristics, and access methods. ๐พ
If a model fits in HBM, it only means that there is enough video memory for it. But it tells you very little about how many bytes need to be transferred, how efficiently memory accesses are combined, how often data is reused, and whether memory is actually limiting the performance of the CUDA kernel.
A good example is FlashAttention. This algorithm calculates the exact attention mechanism, but uses tiling and repeated computations to reduce the amount of data transferred between HBM and the GPU's internal memory. โก๏ธ
Tensor Cores turned out to be another technology that I previously understood too simplistically.
These are specialized computational units for matrix multiplication operations with accumulation, which play a huge role in AI tasks. However, they do not "execute the model" as a whole. ๐ง
Reduction operations, indexing, element-wise calculations, synchronization, and data movement still involve other components of the GPU. In addition, launching CUDA kernels also introduces overhead.
Over time, I stopped asking myself: "Why isn't the GPU 100% utilized?" Instead, I started asking a more accurate question: "What is currently limiting the speed of performing useful work?" ๐ค
Ultimately, all of this turned into a 42-page guide. ๐
It covers streaming multiprocessors (SMs), warps, and schedulers, the memory hierarchy and latency hiding, Tensor Cores, the distribution of GEMM computations across GPU hardware resources, numerical precision, and performance metrics that are very easy to misinterpret.
https://drive.google.com/file/d/17tyvxr__8vZ6o995GNq6I5PC5JOiv0ql/view
#GPU #CUDA #AI #DeepLearning #PerformanceOptimization #TechGuide
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
โญ๏ธ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
I was constantly confusing three different concepts:
โข The amount of work we are launching.
โข The amount of work that is actually placed on the GPU.
โข The amount of work that is ready to be executed right now.
But these are completely different things. โ ๏ธ
Consider a CUDA kernel with 240 blocks, each containing 256 threads. This results in 61,440 logical threads, or 1920 warps. However, the number of launched threads doesn't tell you how many warps can actually be present on the GPU simultaneously. ๐งฎ
Blocks are placed on streaming multiprocessors (SMs) based on the available registers, shared memory, and limitations on the number of threads, warps, and blocks.
Even after being placed on an SM, a warp can be waiting for a memory operation to complete, a dependency between arithmetic operations to be resolved, or for synchronization. The scheduler can only select warps that are actually ready to issue instructions for execution. โณ
This changed my approach to analyzing performance metrics. ๐
Occupancy is a measure of the ratio of the number of active warps on an SM to the maximum number of warps that the SM can support.
GPU utilization measures something different. In NVML, this metric reflects the percentage of the measurement interval during which at least one CUDA kernel was running.
Therefore, the GPU can show 100% utilization even if the Tensor Cores are not being used to their full potential, the HBM bandwidth is not saturated, and performance is limited by one of the system components. At the same time, a significant portion of the GPU's resources may remain underutilized. ๐
Similar misconceptions arise with memory. Registers, shared memory, L1, L2, and HBM are not simply levels of memory that differ in speed. Each has its own areas of accessibility, capacity, management characteristics, and access methods. ๐พ
If a model fits in HBM, it only means that there is enough video memory for it. But it tells you very little about how many bytes need to be transferred, how efficiently memory accesses are combined, how often data is reused, and whether memory is actually limiting the performance of the CUDA kernel.
A good example is FlashAttention. This algorithm calculates the exact attention mechanism, but uses tiling and repeated computations to reduce the amount of data transferred between HBM and the GPU's internal memory. โก๏ธ
Tensor Cores turned out to be another technology that I previously understood too simplistically.
These are specialized computational units for matrix multiplication operations with accumulation, which play a huge role in AI tasks. However, they do not "execute the model" as a whole. ๐ง
Reduction operations, indexing, element-wise calculations, synchronization, and data movement still involve other components of the GPU. In addition, launching CUDA kernels also introduces overhead.
Over time, I stopped asking myself: "Why isn't the GPU 100% utilized?" Instead, I started asking a more accurate question: "What is currently limiting the speed of performing useful work?" ๐ค
Ultimately, all of this turned into a 42-page guide. ๐
It covers streaming multiprocessors (SMs), warps, and schedulers, the memory hierarchy and latency hiding, Tensor Cores, the distribution of GEMM computations across GPU hardware resources, numerical precision, and performance metrics that are very easy to misinterpret.
https://drive.google.com/file/d/17tyvxr__8vZ6o995GNq6I5PC5JOiv0ql/view
#GPU #CUDA #AI #DeepLearning #PerformanceOptimization #TechGuide
โจ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk
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โค3๐2
Forwarded from Machine Learning with Python
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๐ 637 interactive modules on ML and AI
On VizLearn, you can visually explore:
๐ซก Gradient descent;
๐ซก SVM and PCA;
๐ซก Bayes' theorem;
๐ซก BPE tokenization;
๐ซก Query, Key, and Value;
๐ซก KV cache;
๐ซก Quantization;
๐ซก Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
โ Link to the tutorial
https://vizlearn.in/
On VizLearn, you can visually explore:
๐ซก Gradient descent;
๐ซก SVM and PCA;
๐ซก Bayes' theorem;
๐ซก BPE tokenization;
๐ซก Query, Key, and Value;
๐ซก KV cache;
๐ซก Quantization;
๐ซก Neural networks and LLMs.
Change the input data and immediately see the results of the calculations.
โ Link to the tutorial
https://vizlearn.in/
โค2
Agentic AI Roadmap 2026 ๐
๐ https://t.me/DataAnalyticsX
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โค5
Forwarded from Python Courses & Resources
๐ RESEARCH & DIGITAL SOLUTIONS โ YOUR PARTNER IN SUCCESS!
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Are you a researcher, student, business owner, or developer looking for professional support? We are here to help you turn your ideas into real results! ๐ก
๐ฌ 1. Scientific Research & Academic Publishing
Professional review and evaluation of scientific manuscripts before submission.
Guidance on journal selection and publication strategies.
Expert advice to improve research quality and presentation.
Support for researchers throughout the academic publishing process.
๐ป 2. Website & Software Development
Professional website design and development for businesses and individuals.
Custom electronic systems tailored to your needs.
Technical assistance for students and academic projects.
โ๏ธ 3. API & User Interface (UI) Services
API development and integration.
Modern, responsive, and user-friendly interface design.
Seamless integration between applications and systems.
๐ 4. Real-World & Synthetic Datasets
Providing real-world datasets for research and analysis.
Creating customized synthetic datasets according to your requirements.
Data support for scientific research, machine learning, and AI projects.
๐ค 5. FREE Discussion & Consultation
Discuss your research ideas and technical projects with us.
Get initial guidance and recommendations.
Explore the best solutions for your academic or professional needs.
๐ WHY CHOOSE US?
โ Research and technology expertise in one place.
โ Tailored solutions based on your needs.
โ Professional guidance and practical support.
โ Free initial discussion and consultation.
๐ฉ CONTACT US ON TELEGRAM
๐จโ๐ป Team Admin & Support: @Omidyzd62
๐ฌ Feel free to contact us to discuss your ideas, ask questions, or explore how we can help!
Your Research. Your Ideas. Our Expertise. Your Success. ๐ฏ
#ResearchSupport #AcademicPublishing #ScientificResearch #WebDevelopment #SoftwareDevelopment #API #UIUX #Datasets #MachineLearning #ArtificialIntelligence #StudentSupport
โค2
Forwarded from Machine Learning with Python
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Stop paying full price on Udemy. Seriously. ๐ธ
I built a bot that hunts down 100% FREE Udemy coupons 24/7 โ while you sleep, eat, or scroll. ๐ฏ
Here's the magic:
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Forwarded from Machine Learning with Python
๐ 500+ Cheat Sheets on Machine Learning
This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
โ You can find it here
https://github.com/gurovic/MLCheatSheets
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This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.
Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.
โ You can find it here
https://github.com/gurovic/MLCheatSheets
#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI
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โค2
Forwarded from Machine Learning with Python
Oxford University has launched a free course on "Deep Learning Theory" for the 2026-2027 academic year.
The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.
The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.
The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.
In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.
https://courses.maths.ox.ac.uk/course/view.php?id=6621
https://t.me/CodeProgrammer
The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.
The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.
The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.
In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.
https://courses.maths.ox.ac.uk/course/view.php?id=6621
https://t.me/CodeProgrammer
โค4
๐ฅ Free IT & Dev Study Kits โ 2026 Prep & Double 11 Giveaway
Prepping for cloud, dev, or AI roles this year? These free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams.
๐ Cert E-Book โ Cisco & AWS fundamentals, ideal for first-time cert takers
โ https://tinyurl.com/ynutkhec
๐ช Online Free Course โ a structured path that works great as a refresher before exams
โ https://tinyurl.com/4kvuk4m6
โ๏ธ Free AI Materials โ from LLM basics to fine-tuning practice notebooks
โ https://tinyurl.com/2z46z8f4
๐ Cloud Study Guide โ cheat-sheet style, handy for interview prep
โ https://tinyurl.com/ymncyj5k
๐ง Free Mock Exam โ test yourself before booking the real thing
โ https://tinyurl.com/y7ffwecz
๐ Double 11 Giveaway โ win iPhone Duo & Amazon Gift Cards
๐ Save this or forward it to your dev/study group โ good resources are worth sharing. ๐ช
๐ Join Community to Draw: https://chat.whatsapp.com/KkbnOUuS55aJdZMtTGrVYL
๐ฒ Personalized help: https://wa.link/xd2
Prepping for cloud, dev, or AI roles this year? These free kits cover the gaps most people skip โ cert basics, hands-on labs, and mock exams.
๐ Cert E-Book โ Cisco & AWS fundamentals, ideal for first-time cert takers
โ https://tinyurl.com/ynutkhec
๐ช Online Free Course โ a structured path that works great as a refresher before exams
โ https://tinyurl.com/4kvuk4m6
โ๏ธ Free AI Materials โ from LLM basics to fine-tuning practice notebooks
โ https://tinyurl.com/2z46z8f4
๐ Cloud Study Guide โ cheat-sheet style, handy for interview prep
โ https://tinyurl.com/ymncyj5k
๐ง Free Mock Exam โ test yourself before booking the real thing
โ https://tinyurl.com/y7ffwecz
๐ Double 11 Giveaway โ win iPhone Duo & Amazon Gift Cards
๐ Save this or forward it to your dev/study group โ good resources are worth sharing. ๐ช
๐ Join Community to Draw: https://chat.whatsapp.com/KkbnOUuS55aJdZMtTGrVYL
๐ฒ Personalized help: https://wa.link/xd2
โค5
Forwarded from Machine Learning
Columbia University has published a detailed educational resource on the Transformer architecture ๐ .
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.
Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.
The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.
https://www.columbia.edu/~jsl2239/transformers.html
https://t.me/MachineLearning9
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โค2
Forwarded from Machine Learning with Python
๐จ SURPRISE ALERT! ๐จ
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Most people pay $200+ for courses you can grab for $0 โ if you know where to look. Now you have a bot that does the looking for you. โก
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Stop paying full price on Udemy. Seriously. ๐ธ
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Most people pay $200+ for courses you can grab for $0 โ if you know where to look. Now you have a bot that does the looking for you. โก
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โค1
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โค1
If you trade cryptocurrencies and are feeling confused about the current market, I strongly recommend you join the group Iโve mentioned
The group shares the following content every day:
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The group shares the following content every day:
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Whether youโre a beginner or an experienced trader, youโre welcome to join the group to exchange ideas and gain useful insights
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