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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
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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.
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In addition to Gatsbi Reviewer, you can also use:
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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?
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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
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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.iss.one/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.iss.one/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©
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Have you ever published a scientific research paper in a scientific journal?
Final Results
28%
Yes π
72%
No π
β€1
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.iss.one/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.iss.one/DataScienceY
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