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Forwarded from Python Courses & Resources
This channels is for Programmers, Coders, Software Engineers.
0๏ธโฃ Python
1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
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Forwarded from Machine Learning
๐ Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.
๐ Initial Dataset
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https://t.me/DataScienceM
Understanding the ML pipeline is essential to build scalable, production-grade models.
๐ Initial Dataset
Start with raw data. Apply cleaning, curation, and drop irrelevant or redundant features.
Example: Drop constant features or remove columns with 90% missing values.
๐ Exploratory Data Analysis (EDA)
Use mean, median, standard deviation, correlation, and missing value checks.
Techniques like PCA and LDA help with dimensionality reduction.
Example: Use PCA to reduce 50 features down to 10 while retaining 95% variance.
๐ Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.
๐ Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.
๐ Learning Algorithms
Apply algorithms like SVM, Logistic Regression, KNN, Decision Trees, or Ensemble models like Random Forest and Gradient Boosting.
Example: Use Random Forest to capture non-linear interactions in tabular data.
๐ Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
Example: Optimize max_depth and n_estimators in Gradient Boosting.
๐ Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
Example: Drop features with zero importance to reduce overfitting.
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Use cross-validation to evaluate generalization. Train final model on full training set.
Example: 5-fold cross-validation for reliable performance metrics.
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Use task-specific metrics:
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Forwarded from Machine Learning with Python
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Forwarded from Machine Learning with Python
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PhD Students - Do the following before you start writing your thesis.
๐. ๐๐ฌ๐ ๐๐๐ญ๐๐ฑ, ๐ง๐จ๐ญ ๐๐ ๐๐จ๐ซ๐: Writing a 100+ page document in MS word can become a headache. Arranging headings, tables of content, references, etc can become a challenge. So, instead of MS word, use Latex. It will take care of all such things.
๐. ๐๐๐๐ง๐ญ๐ข๐๐ฒ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ซ ๐ญ๐ก๐๐ฌ๐๐ฌ: Before starting your thesis, identify 10-15 exemplar theses that is within your research area or the PhD is carried out in a similar fashion as yours. Skim through them especially the first chapter to understand how to structure your thesis.
๐. ๐๐ฎ๐ข๐ฅ๐ ๐ ๐ฌ๐ญ๐จ๐ซ๐ฒ: During your PhD, you work on different papers that might not be totally linked in a straightforward way. Put these different pieces in front of yourself and think about how to make them link with each other and make a smooth story.
๐. ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐ญ๐ก๐ ๐ฆ๐๐ค๐ ๐จ๐ซ ๐๐ซ๐๐๐ค: This chapter summarizes your whole thesis and leaves an impression on the reader/examiner. Invest the most amount of time in writing this chapter. Amongst others, clearly mention upfront the research papers you have published during your PhD.
๐. ๐๐ซ๐ข๐ฌ๐ฉ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ ๐ฌ๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐จ๐ง๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง๐ฌ: Mention within 3-4 lines the concrete problem you have solved during your PhD. Also, examiners look for 3-4 solid contributions. Don't make them search for them. Present these contributions upfront in the Introduction chapter.
๐. ๐๐ก๐๐ฌ๐ข๐ฌ ๐จ๐ซ๐ ๐๐ง๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐ฏ๐ข๐ ๐ ๐๐ข๐ ๐ฎ๐ซ๐: PhD thesis is a very long document. Navigating through it can be a challenge. Include a figure in the Introduction section that shows the organization of the thesis including the various chapters. You can check my PhD thesis for such a figure.
๐. ๐๐ฅ๐๐๐ซ๐ฅ๐ฒ ๐ฆ๐๐ง๐ญ๐ข๐จ๐ง ๐ฒ๐จ๐ฎ๐ซ ๐ฉ๐ฎ๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ: If you have published some of your research, mention it upfront in your thesis. This shows to reviewers that part of your research has already been peer-reviewed.
๐. ๐๐๐๐ค ๐๐๐๐๐๐๐๐ค: Manage your writing in a way that each part gets reviewed. If you are running short of time, you can send each chapter separately as it completes to your supervisors for feedback.
๐. ๐๐ก๐จ๐ซ๐จ๐ฎ๐ ๐ก๐ฅ๐ฒ ๐ฉ๐ซ๐จ๐จ๐๐ซ๐๐๐: One of the most common comments from thesis reviewers is to fix the typos. Proofread your entire thesis a couple of times before submission to avoid getting this comment.
๐๐. ๐๐ข๐ง๐ค ๐๐ก๐๐ฉ๐ญ๐๐ซ๐ฌ ๐ญ๐จ ๐๐๐๐ก ๐จ๐ญ๐ก๐๐ซ: Make sure that the chapters are linked together. For example, it shouldn't appear that when the reviewer starts reading chapter 4, it is completely different from chapter 3.
๐. ๐๐ฌ๐ ๐๐๐ญ๐๐ฑ, ๐ง๐จ๐ญ ๐๐ ๐๐จ๐ซ๐: Writing a 100+ page document in MS word can become a headache. Arranging headings, tables of content, references, etc can become a challenge. So, instead of MS word, use Latex. It will take care of all such things.
๐. ๐๐๐๐ง๐ญ๐ข๐๐ฒ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐๐ซ ๐ญ๐ก๐๐ฌ๐๐ฌ: Before starting your thesis, identify 10-15 exemplar theses that is within your research area or the PhD is carried out in a similar fashion as yours. Skim through them especially the first chapter to understand how to structure your thesis.
๐. ๐๐ฎ๐ข๐ฅ๐ ๐ ๐ฌ๐ญ๐จ๐ซ๐ฒ: During your PhD, you work on different papers that might not be totally linked in a straightforward way. Put these different pieces in front of yourself and think about how to make them link with each other and make a smooth story.
๐. ๐๐ง๐ญ๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐ญ๐ก๐ ๐ฆ๐๐ค๐ ๐จ๐ซ ๐๐ซ๐๐๐ค: This chapter summarizes your whole thesis and leaves an impression on the reader/examiner. Invest the most amount of time in writing this chapter. Amongst others, clearly mention upfront the research papers you have published during your PhD.
๐. ๐๐ซ๐ข๐ฌ๐ฉ ๐ฉ๐ซ๐จ๐๐ฅ๐๐ฆ ๐ฌ๐ญ๐๐ญ๐๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐จ๐ง๐ญ๐ซ๐ข๐๐ฎ๐ญ๐ข๐จ๐ง๐ฌ: Mention within 3-4 lines the concrete problem you have solved during your PhD. Also, examiners look for 3-4 solid contributions. Don't make them search for them. Present these contributions upfront in the Introduction chapter.
๐. ๐๐ก๐๐ฌ๐ข๐ฌ ๐จ๐ซ๐ ๐๐ง๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐ฏ๐ข๐ ๐ ๐๐ข๐ ๐ฎ๐ซ๐: PhD thesis is a very long document. Navigating through it can be a challenge. Include a figure in the Introduction section that shows the organization of the thesis including the various chapters. You can check my PhD thesis for such a figure.
๐. ๐๐ฅ๐๐๐ซ๐ฅ๐ฒ ๐ฆ๐๐ง๐ญ๐ข๐จ๐ง ๐ฒ๐จ๐ฎ๐ซ ๐ฉ๐ฎ๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ: If you have published some of your research, mention it upfront in your thesis. This shows to reviewers that part of your research has already been peer-reviewed.
๐. ๐๐๐๐ค ๐๐๐๐๐๐๐๐ค: Manage your writing in a way that each part gets reviewed. If you are running short of time, you can send each chapter separately as it completes to your supervisors for feedback.
๐. ๐๐ก๐จ๐ซ๐จ๐ฎ๐ ๐ก๐ฅ๐ฒ ๐ฉ๐ซ๐จ๐จ๐๐ซ๐๐๐: One of the most common comments from thesis reviewers is to fix the typos. Proofread your entire thesis a couple of times before submission to avoid getting this comment.
๐๐. ๐๐ข๐ง๐ค ๐๐ก๐๐ฉ๐ญ๐๐ซ๐ฌ ๐ญ๐จ ๐๐๐๐ก ๐จ๐ญ๐ก๐๐ซ: Make sure that the chapters are linked together. For example, it shouldn't appear that when the reviewer starts reading chapter 4, it is completely different from chapter 3.
โค6
Forwarded from Machine Learning with Python
Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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PhD Students - Do you want to learn how to do research?
If yes, check out these free resources.
๐๐ฅ๐ฌ๐๐ฏ๐ข๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก๐๐ซ ๐๐๐๐๐๐ฆ๐ฒ offers free resources.
Here are 10 of my favorite resources.
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐ ๐ฅ๐ข๐ญ๐๐ซ๐๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฏ๐ข๐๐ฐ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What reviewers look for in a literature review?
- How to conceptualize and write a review?
- Common myths about literature reviews.
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๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ ๐๐ฉ๐ฌ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What is research gap?
- Steps for identifying research gap.
- Tools for identifying research gap.
Link: https://lnkd.in/gvxgD95D
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐๐จ๐ง๐๐ฎ๐๐ญ ๐๐ฏ๐ข๐๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What is evidence-based research?
- Stepwise approach for conducting evidence-based research.
- How to enhance reliability of your research?
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๐. ๐๐จ๐ฐ ๐ญ๐จ ๐๐ข๐ง๐ ๐ซ๐๐ฅ๐๐ฏ๐๐ง๐ญ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ฉ๐๐ฉ๐๐ซ๐ฌ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How to conduct basic search?
- Save searches and set up alerts.
- How to download and export searches?
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๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐๐ง ๐๐๐ฌ๐ญ๐ซ๐๐๐ญ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Why a good abstract is important?
- What is ideal length of abstract?
- What to include in the abstract?
Link: https://lnkd.in/g9drbDZF
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฌ๐๐๐ฎ๐ซ๐ ๐๐ฎ๐ง๐๐ข๐ง๐ ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How to write grant application?
- What funders look for in grant applications?
- Tips for winning grants.
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๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฎ๐ฌ๐ ๐๐๐ง ๐๐ ๐ข๐ง ๐ซ๐๐ฌ๐๐๐ซ๐๐ก (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How has Gen AI impacted research?
- How to use Scopus AI search tool?
- Future of Gen AI in research
Link: https://lnkd.in/gepXEzBf
๐. ๐๐ฎ๐ญ๐ก๐จ๐ซ ๐ฉ๐จ๐ฅ๐ข๐๐ข๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ฎ๐ฌ๐ ๐จ๐ ๐๐๐ง๐๐ซ๐๐ญ๐ ๐๐ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Ethical cases related to Generative AI
- Opportunities offered by Generative AI
- Risks posed by Generative AI
Link: https://lnkd.in/gT4Xg7yP
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐๐จ๐ฏ๐๐ซ ๐ฅ๐๐ญ๐ญ๐๐ซ ๐๐จ๐ซ ๐ฒ๐จ๐ฎ๐ซ ๐ฆ๐๐ง๐ฎ๐ฌ๐๐ซ๐ข๐ฉ๐ญ (๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Importance of a good cover letter
- How to write strong cover letter?
- What to include in the cover letter?
Link: https://lnkd.in/gFA_pNkD
๐๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ซ๐๐ฌ๐ฉ๐จ๐ง๐ ๐ญ๐จ ๐ซ๐๐ฏ๐ข๐๐ฐ๐๐ซ๐ฌโ ๐๐จ๐ฆ๐ฆ๐๐ง๐ญ๐ฌ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Understanding reviewersโ comments
- How to write response to each comment?
- How to increase your chances of paper acceptance?
Link: https://lnkd.in/gcG7mxrc
If yes, check out these free resources.
๐๐ฅ๐ฌ๐๐ฏ๐ข๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก๐๐ซ ๐๐๐๐๐๐ฆ๐ฒ offers free resources.
Here are 10 of my favorite resources.
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐ ๐ฅ๐ข๐ญ๐๐ซ๐๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฏ๐ข๐๐ฐ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What reviewers look for in a literature review?
- How to conceptualize and write a review?
- Common myths about literature reviews.
Link: https://lnkd.in/gRsWFvdy
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ข๐๐๐ง๐ญ๐ข๐๐ฒ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ ๐๐ฉ๐ฌ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What is research gap?
- Steps for identifying research gap.
- Tools for identifying research gap.
Link: https://lnkd.in/gvxgD95D
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐๐จ๐ง๐๐ฎ๐๐ญ ๐๐ฏ๐ข๐๐๐ง๐๐-๐๐๐ฌ๐๐ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- What is evidence-based research?
- Stepwise approach for conducting evidence-based research.
- How to enhance reliability of your research?
Link: https://lnkd.in/gMUxXybm
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐๐ข๐ง๐ ๐ซ๐๐ฅ๐๐ฏ๐๐ง๐ญ ๐ซ๐๐ฌ๐๐๐ซ๐๐ก ๐ฉ๐๐ฉ๐๐ซ๐ฌ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How to conduct basic search?
- Save searches and set up alerts.
- How to download and export searches?
Link: https://lnkd.in/gKXauFHr
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐๐ง ๐๐๐ฌ๐ญ๐ซ๐๐๐ญ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Why a good abstract is important?
- What is ideal length of abstract?
- What to include in the abstract?
Link: https://lnkd.in/g9drbDZF
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฌ๐๐๐ฎ๐ซ๐ ๐๐ฎ๐ง๐๐ข๐ง๐ ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How to write grant application?
- What funders look for in grant applications?
- Tips for winning grants.
Link: https://lnkd.in/g-diuMPv
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฎ๐ฌ๐ ๐๐๐ง ๐๐ ๐ข๐ง ๐ซ๐๐ฌ๐๐๐ซ๐๐ก (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- How has Gen AI impacted research?
- How to use Scopus AI search tool?
- Future of Gen AI in research
Link: https://lnkd.in/gepXEzBf
๐. ๐๐ฎ๐ญ๐ก๐จ๐ซ ๐ฉ๐จ๐ฅ๐ข๐๐ข๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ฎ๐ฌ๐ ๐จ๐ ๐๐๐ง๐๐ซ๐๐ญ๐ ๐๐ (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Ethical cases related to Generative AI
- Opportunities offered by Generative AI
- Risks posed by Generative AI
Link: https://lnkd.in/gT4Xg7yP
๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ ๐๐จ๐ฏ๐๐ซ ๐ฅ๐๐ญ๐ญ๐๐ซ ๐๐จ๐ซ ๐ฒ๐จ๐ฎ๐ซ ๐ฆ๐๐ง๐ฎ๐ฌ๐๐ซ๐ข๐ฉ๐ญ (๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Importance of a good cover letter
- How to write strong cover letter?
- What to include in the cover letter?
Link: https://lnkd.in/gFA_pNkD
๐๐. ๐๐จ๐ฐ ๐ญ๐จ ๐ซ๐๐ฌ๐ฉ๐จ๐ง๐ ๐ญ๐จ ๐ซ๐๐ฏ๐ข๐๐ฐ๐๐ซ๐ฌโ ๐๐จ๐ฆ๐ฆ๐๐ง๐ญ๐ฌ? (๐๐ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐๐จ)
You will learn
- Understanding reviewersโ comments
- How to write response to each comment?
- How to increase your chances of paper acceptance?
Link: https://lnkd.in/gcG7mxrc
โค6๐1
Forwarded from Machine Learning with Python
PhD Students - Do you need datasets for your research?
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets โ object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset โ visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset โ dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation โ clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset โ action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
https://t.me/CodeProgrammer๐พ
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets โ object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset โ visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset โ dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation โ clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset โ action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
https://t.me/CodeProgrammer
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โค4
Forwarded from Machine Learning with Python
Rocket.new lets you build a full website using prompts with their vibe solutioning platform ๐ง โก๏ธ
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โค1
PhD Students - Here is an example of a good discussion section.
A good discussion section should answer 6 questions.
1. What is different in your findings compared to previous research?
2. What is similar in your findings compared to previous research?
3. How different sections of your results section correlate?
4. What are the implications of your findings for practitioners?
5. What are the implications of your findings for researchers?
6. What are the limitations or threats to the validity of your findings?
A good discussion section should answer 6 questions.
1. What is different in your findings compared to previous research?
2. What is similar in your findings compared to previous research?
3. How different sections of your results section correlate?
4. What are the implications of your findings for practitioners?
5. What are the implications of your findings for researchers?
6. What are the limitations or threats to the validity of your findings?
โค5
Forwarded from Machine Learning with Python
A collection of basic techniques for working with tensors in PyTorch โ for those who are starting to get acquainted with the framework and want to quickly master its fundamentals.
What's inside:
A good starting material to understand the mechanics of tensors before moving on to models and training.โถ๏ธ What tensors are and why they are neededโถ๏ธ Tensor initialization: zeros, ones, random, similar sizeโถ๏ธ Type conversion and switching between NumPy and PyTorchโถ๏ธ Arithmetic, logical operations, tensor comparisonโถ๏ธ Matrix multiplication and batch computationsโถ๏ธ Broadcasting, view(), reshape(), changing dimensionsโถ๏ธ Indexing and slicing: how to access parts of a tensorโถ๏ธ Notebook with code examples
tags: #useful
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โค2