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PhD students — Do these 10 things in the first year of your PhD.

PhD Students - Do the following before you start writing your thesis.


𝟏. 𝐔𝐬𝐞 𝐋𝐚𝐭𝐞𝐱, 𝐧𝐨𝐭 𝐌𝐒 𝐖𝐨𝐫𝐝: 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.

𝟐. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐞𝐱𝐞𝐦𝐩𝐥𝐚𝐫 𝐭𝐡𝐞𝐬𝐞𝐬: 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.

𝟑. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐬𝐭𝐨𝐫𝐲: 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.

𝟒. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐚𝐤𝐞 𝐨𝐫 𝐛𝐫𝐞𝐚𝐤: 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.

𝟓. 𝐂𝐫𝐢𝐬𝐩 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: 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.

𝟔. 𝐓𝐡𝐞𝐬𝐢𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐯𝐢𝐚 𝐚 𝐟𝐢𝐠𝐮𝐫𝐞: 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.

𝟕. 𝐂𝐥𝐞𝐚𝐫𝐥𝐲 𝐦𝐞𝐧𝐭𝐢𝐨𝐧 𝐲𝐨𝐮𝐫 𝐩𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: 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.

𝟖. 𝐒𝐞𝐞𝐤 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤: 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.

𝟗. 𝐓𝐡𝐨𝐫𝐨𝐮𝐠𝐡𝐥𝐲 𝐩𝐫𝐨𝐨𝐟𝐫𝐞𝐚𝐝: 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.

𝟏𝟎. 𝐋𝐢𝐧𝐤 𝐜𝐡𝐚𝐩𝐭𝐞𝐫𝐬 𝐭𝐨 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫: 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.

𝐄𝐥𝐬𝐞𝐯𝐢𝐞𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 𝐀𝐜𝐚𝐝𝐞𝐦𝐲 offers free resources.

Here are 10 of my favorite resources.

𝟏. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰 (𝟔𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟐. 𝐇𝐨𝐰 𝐭𝐨 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐠𝐚𝐩𝐬 (𝟏𝟐 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- What is research gap?

- Steps for identifying research gap.

- Tools for identifying research gap.

Link: https://lnkd.in/gvxgD95D

𝟑. 𝐇𝐨𝐰 𝐭𝐨 𝐜𝐨𝐧𝐝𝐮𝐜𝐭 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 (𝟑𝟖 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟒. 𝐇𝐨𝐰 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫𝐬? (𝟏𝟓 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟓. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐚𝐧 𝐚𝐛𝐬𝐭𝐫𝐚𝐜𝐭 (𝟒𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟔. 𝐇𝐨𝐰 𝐭𝐨 𝐬𝐞𝐜𝐮𝐫𝐞 𝐟𝐮𝐧𝐝𝐢𝐧𝐠? (𝟔𝟎 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟕. 𝐇𝐨𝐰 𝐭𝐨 𝐮𝐬𝐞 𝐆𝐞𝐧 𝐀𝐈 𝐢𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 (𝟓𝟑 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟖. 𝐀𝐮𝐭𝐡𝐨𝐫 𝐩𝐨𝐥𝐢𝐜𝐢𝐞𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐮𝐬𝐞 𝐨𝐟 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐀𝐈 (𝟏𝟏 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Ethical cases related to Generative AI

- Opportunities offered by Generative AI

- Risks posed by Generative AI

Link: https://lnkd.in/gT4Xg7yP

𝟗. 𝐇𝐨𝐰 𝐭𝐨 𝐰𝐫𝐢𝐭𝐞 𝐜𝐨𝐯𝐞𝐫 𝐥𝐞𝐭𝐭𝐞𝐫 𝐟𝐨𝐫 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐮𝐬𝐜𝐫𝐢𝐩𝐭 (𝟖 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

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

𝟏𝟎. 𝐇𝐨𝐰 𝐭𝐨 𝐫𝐞𝐬𝐩𝐨𝐧𝐝 𝐭𝐨 𝐫𝐞𝐯𝐢𝐞𝐰𝐞𝐫𝐬’ 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬? (𝟑𝟕 𝐦𝐢𝐧 𝐯𝐢𝐝𝐞𝐨)

You will learn

- Understanding reviewers’ comments

- How to write response to each comment?

- How to increase your chances of paper acceptance?

Link: https://lnkd.in/gcG7mxrc

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?

PhD Students - Which tense to use in your research papers?

PhD Students – How to compare 10 papers in 10 seconds?

Meet 𝐒𝐜𝐢𝐒𝐩𝐚𝐜𝐞 – this tool compares papers for you.

Here is how it works.

1. Go to https://lnkd.in/dyirEcYG and log in

2. Click on + 𝑠𝑖𝑔𝑛 and upload the 10 papers.

3. After uploading papers, write your prompt.

𝐶𝑜𝑚𝑝𝑎𝑟𝑒 𝑡ℎ𝑒 𝑢𝑝𝑙𝑜𝑎𝑑𝑒𝑑 10 𝑟𝑒𝑠𝑒𝑎𝑟𝑐ℎ 𝑝𝑎𝑝𝑒𝑟𝑠

4. SciSpace will start comparing the papers.

5. You will see the comparison result on right side.

6. Here you will see various insights with paper numbers.

7. At the end, you will see summary of the comparison.

8. SciSpace compares the papers based on:

✓ Similarities in research themes
✓ Differences in approaches
✓ Relative strengths and weaknesses
✓ Gaps identified across papers
✓ Relationships and building upon each other

9. To trace to each paper, click on the 𝑝𝑎𝑝𝑒𝑟 𝑛𝑢𝑚𝑏𝑒𝑟𝑠

10. To trace to exact location, click on 𝑙𝑜𝑐𝑎𝑡𝑒 𝑃𝐷𝐹.

Where can you use such comparison?

You can use it to:

➝ Understand the related literature.
➝ Position the novelty of your research paper.
➝ Understand niche questions in a research area.
➝ Grasp key insights from a bunch of papers in one go.

Try SciSpace today: https://lnkd.in/dyirEcYG

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PhD Students – How to find references for your paper in seconds?

Meet 𝐋𝐢𝐧𝐞𝐫 – a tool that inserts citations in paper.

𝐇𝐨𝐰 𝐋𝐢𝐧𝐞𝐫 𝐰𝐨𝐫𝐤𝐬?

1. Go to https://lnkd.in/dsgKZV-P
2. Click on 𝐶𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑒𝑟 from the left menu
3. Paste the text in which you want to insert citations
4. Now click on 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒 𝐶𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑠
5. Liner will insert citations in your text

𝐔𝐬𝐢𝐧𝐠 𝐋𝐢𝐧𝐞𝐫 𝐟𝐨𝐫 𝐜𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬 𝐡𝐚𝐬 𝟒 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞𝐬

➝ Unlike ChatGPT, it recommends reliable citations
➝ The whole process is very transparent
➝ The citations automatically get inserted in your text
➝ The process is very quick and super easy

Try Liner today for citations: https://lnkd.in/dsgKZV-P

𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.

Vision Transformers threw that whole approach out.

ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.

That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.

𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:

- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification

The Image below shows

Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.

The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.

Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.

The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.

𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬
ViT paper dissection
https://youtube.com/watch?v=U_sdodhcBC4

Build ViT from Scratch
https://youtube.com/watch?v=ZRo74xnN2SI

Original Paper
https://arxiv.org/abs/2010.11929

https://t.me/CodeProgrammer

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