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Forwarded from Machine Learning
๐Ÿš€ Machine Learning Workflow: Step-by-Step Breakdown
Understanding the ML pipeline is essential to build scalable, production-grade models.

๐Ÿ‘‰ 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.

๐Ÿ‘‰ 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.

๐Ÿ‘‰ Input Variables
Structured table with features like ID, Age, Income, Loan Status, etc.
Ensure numeric encoding and feature engineering are complete before training.

๐Ÿ‘‰ Processed Dataset
Split the data into training (70%) and testing (30%) sets.
Example: Stratified sampling ensures target distribution consistency.

๐Ÿ‘‰ 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.

๐Ÿ‘‰ Hyperparameter Optimization
Tune parameters using Grid Search or Random Search for better performance.
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๐Ÿ‘‰ Feature Selection
Use model-based importance ranking (e.g., from Random Forest) to remove noisy or irrelevant features.
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Use cross-validation to evaluate generalization. Train final model on full training set.
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- Classification โ€“ MCC, Sensitivity, Specificity, Accuracy
- Regression โ€“ RMSE, Rยฒ, MSE
Example: For imbalanced classes, prefer MCC over simple accuracy.

๐Ÿ’ก This workflow ensures models are robust, interpretable, and ready for deployment in real-world applications.

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PhD students โ€” Do these 10 things in the first year of your PhD.
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PhD Students - Do the following before you start writing your thesis.


๐Ÿ. ๐”๐ฌ๐ž ๐‹๐š๐ญ๐ž๐ฑ, ๐ง๐จ๐ญ ๐Œ๐’ ๐–๐จ๐ซ๐: 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Ÿ. ๐ˆ๐๐ž๐ง๐ญ๐ข๐Ÿ๐ฒ ๐ž๐ฑ๐ž๐ฆ๐ฉ๐ฅ๐š๐ซ ๐ญ๐ก๐ž๐ฌ๐ž๐ฌ: 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Ÿ‘. ๐๐ฎ๐ข๐ฅ๐ ๐š ๐ฌ๐ญ๐จ๐ซ๐ฒ: 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Ÿ’. ๐ˆ๐ง๐ญ๐ซ๐จ๐๐ฎ๐œ๐ญ๐ข๐จ๐ง ๐ข๐ฌ ๐ญ๐ก๐ž ๐ฆ๐š๐ค๐ž ๐จ๐ซ ๐›๐ซ๐ž๐š๐ค: 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Ÿ“. ๐‚๐ซ๐ข๐ฌ๐ฉ ๐ฉ๐ซ๐จ๐›๐ฅ๐ž๐ฆ ๐ฌ๐ญ๐š๐ญ๐ž๐ฆ๐ž๐ง๐ญ ๐š๐ง๐ ๐œ๐จ๐ง๐ญ๐ซ๐ข๐›๐ฎ๐ญ๐ข๐จ๐ง๐ฌ: 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Ÿ”. ๐“๐ก๐ž๐ฌ๐ข๐ฌ ๐จ๐ซ๐ ๐š๐ง๐ข๐ณ๐š๐ญ๐ข๐จ๐ง ๐ฏ๐ข๐š ๐š ๐Ÿ๐ข๐ ๐ฎ๐ซ๐ž: 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Ÿ•. ๐‚๐ฅ๐ž๐š๐ซ๐ฅ๐ฒ ๐ฆ๐ž๐ง๐ญ๐ข๐จ๐ง ๐ฒ๐จ๐ฎ๐ซ ๐ฉ๐ฎ๐›๐ฅ๐ข๐œ๐š๐ญ๐ข๐จ๐ง๐ฌ: 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Ÿ–. ๐’๐ž๐ž๐ค ๐Ÿ๐ž๐ž๐๐›๐š๐œ๐ค: 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Ÿ—. ๐“๐ก๐จ๐ซ๐จ๐ฎ๐ ๐ก๐ฅ๐ฒ ๐ฉ๐ซ๐จ๐จ๐Ÿ๐ซ๐ž๐š๐: 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.

๐Ÿ๐ŸŽ. ๐‹๐ข๐ง๐ค ๐œ๐ก๐š๐ฉ๐ญ๐ž๐ซ๐ฌ ๐ญ๐จ ๐ž๐š๐œ๐ก ๐จ๐ญ๐ก๐ž๐ซ: 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.
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PhD Students - Do you want to learn how to do research?

If yes, check out these free resources.

๐„๐ฅ๐ฌ๐ž๐ฏ๐ข๐ž๐ซ ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก๐ž๐ซ ๐€๐œ๐š๐๐ž๐ฆ๐ฒ offers free resources.

Here are 10 of my favorite resources.

๐Ÿ. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ž ๐š ๐ฅ๐ข๐ญ๐ž๐ซ๐š๐ญ๐ฎ๐ซ๐ž ๐ซ๐ž๐ฏ๐ข๐ž๐ฐ (๐Ÿ”๐ŸŽ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ข๐๐ž๐ง๐ญ๐ข๐Ÿ๐ฒ ๐ซ๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก ๐ ๐š๐ฉ๐ฌ (๐Ÿ๐Ÿ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

You will learn

- What is research gap?

- Steps for identifying research gap.

- Tools for identifying research gap.

Link: https://lnkd.in/gvxgD95D

๐Ÿ‘. ๐‡๐จ๐ฐ ๐ญ๐จ ๐œ๐จ๐ง๐๐ฎ๐œ๐ญ ๐ž๐ฏ๐ข๐๐ž๐ง๐œ๐ž-๐›๐š๐ฌ๐ž๐ ๐ซ๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก (๐Ÿ‘๐Ÿ– ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ’. ๐‡๐จ๐ฐ ๐ญ๐จ ๐Ÿ๐ข๐ง๐ ๐ซ๐ž๐ฅ๐ž๐ฏ๐š๐ง๐ญ ๐ซ๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก ๐ฉ๐š๐ฉ๐ž๐ซ๐ฌ? (๐Ÿ๐Ÿ“ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ“. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ž ๐š๐ง ๐š๐›๐ฌ๐ญ๐ซ๐š๐œ๐ญ (๐Ÿ’๐ŸŽ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ”. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ฌ๐ž๐œ๐ฎ๐ซ๐ž ๐Ÿ๐ฎ๐ง๐๐ข๐ง๐ ? (๐Ÿ”๐ŸŽ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ•. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ฎ๐ฌ๐ž ๐†๐ž๐ง ๐€๐ˆ ๐ข๐ง ๐ซ๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก (๐Ÿ“๐Ÿ‘ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ–. ๐€๐ฎ๐ญ๐ก๐จ๐ซ ๐ฉ๐จ๐ฅ๐ข๐œ๐ข๐ž๐ฌ ๐จ๐ง ๐ญ๐ก๐ž ๐ฎ๐ฌ๐ž ๐จ๐Ÿ ๐†๐ž๐ง๐ž๐ซ๐š๐ญ๐ž ๐€๐ˆ (๐Ÿ๐Ÿ ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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Ÿ—. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ฐ๐ซ๐ข๐ญ๐ž ๐œ๐จ๐ฏ๐ž๐ซ ๐ฅ๐ž๐ญ๐ญ๐ž๐ซ ๐Ÿ๐จ๐ซ ๐ฒ๐จ๐ฎ๐ซ ๐ฆ๐š๐ง๐ฎ๐ฌ๐œ๐ซ๐ข๐ฉ๐ญ (๐Ÿ– ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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

๐Ÿ๐ŸŽ. ๐‡๐จ๐ฐ ๐ญ๐จ ๐ซ๐ž๐ฌ๐ฉ๐จ๐ง๐ ๐ญ๐จ ๐ซ๐ž๐ฏ๐ข๐ž๐ฐ๐ž๐ซ๐ฌโ€™ ๐œ๐จ๐ฆ๐ฆ๐ž๐ง๐ญ๐ฌ? (๐Ÿ‘๐Ÿ• ๐ฆ๐ข๐ง ๐ฏ๐ข๐๐ž๐จ)

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
โค6๐Ÿ‘1
PhD Students - Do you need datasets for your research?

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1. Korean Exam Question Dataset for AI Training

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2. Multilingual Grammar Correction Dataset

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3. High quality video caption dataset

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4. 3D models and scenes datasets for AI and simulation

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5. Image editing datasets โ€“ object removal, addition & modification

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6. QA dataset โ€“ visual & text reasoning

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7. English instruction tuning dataset

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8. Large scale vision language dataset for AI training

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9. News dataset

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10. Global building photos dataset

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11. Facial landmarks dataset

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12. 3D Human Pose & Landmarks dataset

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13. 3D Hand Pose & Gesture Recognition dataset

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14. 14. Driver monitoring dataset โ€“ dangerous, fatigue

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15. Japanese handwriting OCR dataset

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16. American English Male voice TTS dataset

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17. Riddles and brain teasers dataset

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18. Chinese test questions text

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19. Chinese medical question answering data

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20. Multi-round interpersonal dialogues text data

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21. Human activity recognition dataset

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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 ๐Ÿ‘พ
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Rocket.new lets you build a full website using prompts with their vibe solutioning platform ๐Ÿง โšก๏ธ
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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?
โค5
PhD Students - Which tense to use in your research papers?
โค2
๐Ÿ PyTorch for Beginners: All the Basics on Tensors in One Place

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:
โ–ถ๏ธ What tensors are and why they are needed

โ–ถ๏ธ Tensor initialization: zeros, ones, random, similar size

โ–ถ๏ธ Type conversion and switching between NumPy and PyTorch

โ–ถ๏ธ Arithmetic, logical operations, tensor comparison

โ–ถ๏ธ Matrix multiplication and batch computations

โ–ถ๏ธ Broadcasting, view(), reshape(), changing dimensions

โ–ถ๏ธ Indexing and slicing: how to access parts of a tensor

โ–ถ๏ธ Notebook with code examples
A good starting material to understand the mechanics of tensors before moving on to models and training.

โ›“ GitHub link

tags: #useful

โžก @codeprogrammer
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