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This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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β Join our IT community: get free study materials, exam tips & peer support
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PhD Students β How to write a systematic literature review draft in 1 day?
A systematic literature review takes 4-6 months.
You can reduce this time.
π― Here is how you can write it in 1 hour.
1οΈβ£ Go to www.gatsbi.com
2οΈβ£ Select Gatsbi reviewer from the drop-down menu
3οΈβ£ Enter the topic of your literature review
4οΈβ£ Gatsbi will generate an outline for review
5οΈβ£ If you are OK with it, click on write manuscript.
6οΈβ£ Gatsbi will write the literature review for you.
π The literature review contains the following parts
β Title
β Abstract
β Introduction
β Methodology
β Results
β Discussion
β Conclusion
β References
π This polished paper also contains
β Diagrams
β Tables
β Equations
β Graphs
Once the paper is ready, you can humanize the text.
Once humanized, you can download it in the following formats.
β³ MS Word
β³ Latex
β³ Markdown
After downloading, you can make any changes you want.
In addition to Gatsbi Reviewer, you can also use:
β Gatsbi Innovator: Generate ideas before writing
β Gatsbi Writer: Write research papers
π Try Gatsbi today for free: www.gatsbi.com
βοΈ Anything you'd like to add?
#phd #research #literature #review
A systematic literature review takes 4-6 months.
You can reduce this time.
π― Here is how you can write it in 1 hour.
1οΈβ£ Go to www.gatsbi.com
2οΈβ£ Select Gatsbi reviewer from the drop-down menu
3οΈβ£ Enter the topic of your literature review
4οΈβ£ Gatsbi will generate an outline for review
5οΈβ£ If you are OK with it, click on write manuscript.
6οΈβ£ Gatsbi will write the literature review for you.
π The literature review contains the following parts
β Title
β Abstract
β Introduction
β Methodology
β Results
β Discussion
β Conclusion
β References
π This polished paper also contains
β Diagrams
β Tables
β Equations
β Graphs
Once the paper is ready, you can humanize the text.
Once humanized, you can download it in the following formats.
β³ MS Word
β³ Latex
β³ Markdown
After downloading, you can make any changes you want.
In addition to Gatsbi Reviewer, you can also use:
β Gatsbi Innovator: Generate ideas before writing
β Gatsbi Writer: Write research papers
π Try Gatsbi today for free: www.gatsbi.com
βοΈ Anything you'd like to add?
#phd #research #literature #review
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4. Data Science: R Basics π»
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6. Introduction to Python π
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7. Writing in the Sciences βοΈ
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8. How to Write and Publish a Scientific Paper π
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9. Statistical Thinking and Data Analysis π
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10. AI for Scientific Research Specialization π€
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Are there any additional topics you would like to include?
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1. Understanding Research Methods π
β³ https://lnkd.in/g-xBFj4v
2. Research Design: Inquiry and Discovery π
β³ https://lnkd.in/dEkr-7tn
3. Quantitative Methods π
β³ https://lnkd.in/dGBf7EvP
4. Data Science: R Basics π»
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5. Research Data Management and Sharing π
β³ https://lnkd.in/dNivuaYT
6. Introduction to Python π
β³ https://lnkd.in/dtWQXSWU
7. Writing in the Sciences βοΈ
β³ https://lnkd.in/dbEran3u
8. How to Write and Publish a Scientific Paper π
β³ https://lnkd.in/giwTe2is
9. Statistical Thinking and Data Analysis π
β³ https://lnkd.in/dMggEddB
10. AI for Scientific Research Specialization π€
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11. Ensuring Visibility π
β³ https://lnkd.in/dTEU8dPM
12. Conference Skills for Researchers π€
β³ https://lnkd.in/dwXkNmpZ
Are there any additional topics you would like to include?
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Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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π Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning
Both code and weights are available under the MIT license on HuggingFace.
π Key details:
β’ Trained from scratch (not a finetune) on proprietary data and infrastructure
β’ Mixture-of-Experts (MoE) architecture
Models:
π§ GigaChat-3.1 Ultra
β’ 702B MoE model for high-performance environments
β’ Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
β’ Supports FP8 training and MTP
β‘οΈ GigaChat-3.1 Lightning
β’ 10B model (1.8B active parameters)
β’ Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
β’ Efficient local inference
β’ Up to 256k context
Engineering highlights:
β’ Custom metric to detect and reduce generation loops
β’ DPO training moved to native FP8
β’ Improvements in post-training pipeline
β’ Identified and fixed a critical issue affecting evaluation quality
π Trained on 14 languages (optimized for English and Russian)
Use cases:
β’ chatbots
β’ AI assistants
β’ copilots
β’ internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
Both code and weights are available under the MIT license on HuggingFace.
π Key details:
β’ Trained from scratch (not a finetune) on proprietary data and infrastructure
β’ Mixture-of-Experts (MoE) architecture
Models:
π§ GigaChat-3.1 Ultra
β’ 702B MoE model for high-performance environments
β’ Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks
β’ Supports FP8 training and MTP
β‘οΈ GigaChat-3.1 Lightning
β’ 10B model (1.8B active parameters)
β’ Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks
β’ Efficient local inference
β’ Up to 256k context
Engineering highlights:
β’ Custom metric to detect and reduce generation loops
β’ DPO training moved to native FP8
β’ Improvements in post-training pipeline
β’ Identified and fixed a critical issue affecting evaluation quality
π Trained on 14 languages (optimized for English and Russian)
Use cases:
β’ chatbots
β’ AI assistants
β’ copilots
β’ internal ML systems
Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.
β€4
Forwarded from Machine Learning with Python
βοΈ 10 Books to Understand How Large Language Models Function (2026)
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. ππ€
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. ππ€
β€4
Forwarded from Machine Learning with Python
π 12 Essential Articles for Data Scientists
π· Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
π· Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
π· Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
π· Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
π· Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
π· Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
π· Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
π· Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
π· Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
π· Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
π· Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
π· Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.me/CodeProgrammerπ
π· Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
π· Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
π· Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
π· Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
π· Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
π· Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
π· Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
π· Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
π· Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
π· Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
π· Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
π· Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.me/CodeProgrammer
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PhD Students: How to humanize AI-generated text in seconds? ππ€
Introducing Humanizethis, a complimentary tool designed to humanize your text. π
Here is the procedure:
1. Navigate to https://humanizethis.io π
2. Copy and paste your text or upload the relevant file π
3. HumanizeThis will process the text to humanize it within seconds β±οΈ
4. Subsequently, the system verifies the content through eight AI detectors, including:
- TurnItIn
- GPTZero
- Originality AI
- CopyLeaks, and others
5. These detectors confirm that the text has been successfully humanized β
6. Users may also review the tracked changes π
7. Approve the modifications and copy the humanized text π
Notably, this service is provided at no cost. π°
#phd #research
Introducing Humanizethis, a complimentary tool designed to humanize your text. π
Here is the procedure:
1. Navigate to https://humanizethis.io π
2. Copy and paste your text or upload the relevant file π
3. HumanizeThis will process the text to humanize it within seconds β±οΈ
4. Subsequently, the system verifies the content through eight AI detectors, including:
- TurnItIn
- GPTZero
- Originality AI
- CopyLeaks, and others
5. These detectors confirm that the text has been successfully humanized β
6. Users may also review the tracked changes π
7. Approve the modifications and copy the humanized text π
Notably, this service is provided at no cost. π°
#phd #research
β€2
Research Papers PHD
PhD Students: How to humanize AI-generated text in seconds? ππ€ Introducing Humanizethis, a complimentary tool designed to humanize your text. π Here is the procedure: 1. Navigate to https://humanizethis.io π 2. Copy and paste your text or upload the relevantβ¦
We provide our services at competitive rates, backed by twenty years of experience. π
Please contact us via @Omidyzd62. π©
Please contact us via @Omidyzd62. π©
β€2
Have you ever published a scientific research paper in a scientific journal?
Final Results
28%
Yes π
72%
No π
Annotated example of a strong research article.
Good papers donβt try to sound smart
They try to make the science clear
A research paper is a guided tour of your thinking
If readers get lost, the writing needs work
Clarity > complexity. Always.
#AcademicWriting #PhDLife #ResearchTips #ScientificWriting #WriteBetter
https://t.me/DataScienceYπ
Good papers donβt try to sound smart
They try to make the science clear
A research paper is a guided tour of your thinking
If readers get lost, the writing needs work
Clarity > complexity. Always.
#AcademicWriting #PhDLife #ResearchTips #ScientificWriting #WriteBetter
https://t.me/DataScienceY
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β€5π1
Today, the public mint for Lobsters on TON goes live on Getgems π¦
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint β it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from βjust an imageβ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
π Mint your Lobster on Getgems π¦π¦π¦
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint β it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from βjust an imageβ into a digital agent.
Public mint: today, 16:00
Price: 50 TON
π Mint your Lobster on Getgems π¦π¦π¦
β€2