🎯 Key Takeaways for quick navigation: 00:13 To *master AI/ML, reading research papers is crucial. Navigating these papers is a skill to stay ahead in the field.* 01:38 Compile *a list of 5-10 papers on a specific topic. Reading them linearly isn't efficient; scanning and setting realistic goals matter.* 02:04 Set *realistic goals based on your level: 15-20 papers for foundational grasp, 50-100 for deep understanding. Start with 5-10 if exploring.* 03:15 Efficiently *scan titles, abstracts, and figures first. Sample about 10% of each paper to decide if it's worth diving into.* 04:35 Skip *sections liberally, focusing on abstracts, intros, and conclusions. Don't get discouraged if some parts are challenging; practice improves understanding.* 05:28 Regularly *reading papers builds a strong knowledge base, making you stand out in the long term. Consider it an investment in your career growth.* 06:21 Recommended *sources for finding research papers: Papers with Code, Distilled AI, Deep Learning Monitor, and Archive Sanity. Each has its pros and cons.* Made with HARPA AI
Do you think one should get completely comfortable with all the prerequisites of a method discussed in the paper? Sometimes there is too much of that (for a not-so-experienced reader)? What kind of code implementation details should you care about, if any, aside from reading the paper?
Sure thing! It's good to understand the basics of a method from a research paper, but you don't have to stress about every little detail, especially if you're not super experienced. Just focus on the main ideas first. When it comes to the code part, try to get the overall picture rather than getting stuck in all the tiny code details. Later on, if you want to, you can dig into the code more if needed
My Q is on implementing research papers as projects. Does that raise your value in the market? If yes, how would you recommend going about building a repository of research worthy of a valuable job?
Research papers raise your value if you get them published. I talked briefly about it here ua-cam.com/video/IzeVmfc73CA/v-deo.html More on them coming in the next ai roadmap video. Stay tuned!
Before reading ,i would highly recommend taking a free course on ML mathematics . But if you have an engineering degree you can skip those , its basically the things they taught you in the Uni (calculus 1,2,3 , probability,statistics,queuing theory etc).
Mam how to do cold calling properly when searching for a job after a big career gap of 5 yrs due to family reasons ? I am an hvac engineer by profession trying to get back into work by looking for different job hunt methods..
This is a great question for our upcoming LinkedIn event. Please ask here and I’ll answer during the q&a Landing Dream Interviews: Mastering LinkedIn for Software Engineers www.linkedin.com/events/masteringlinkedinforsoftwareeng7138971303499235328/
Hey i love your videos ❤ but as a subscriber i wanna give you a suggestion your voice is too sharp please edit the audio just the cut off high pitches then it will be soothing while we listen to this as a podcast 😅😅 ,yeah i actually listen to and learn from you guys while coding 😊
Thanks for the suggestion. I do all the editing myself so I’ll keep take it into consideration. Out of curiosity what device are you watching from? I noticed the sound quality is very different on my iPhone vs computer.
✅Get FREE resources on latest AI research papers
www.exaltitude.io/job-seekers?
🎯 Key Takeaways for quick navigation:
00:13 To *master AI/ML, reading research papers is crucial. Navigating these papers is a skill to stay ahead in the field.*
01:38 Compile *a list of 5-10 papers on a specific topic. Reading them linearly isn't efficient; scanning and setting realistic goals matter.*
02:04 Set *realistic goals based on your level: 15-20 papers for foundational grasp, 50-100 for deep understanding. Start with 5-10 if exploring.*
03:15 Efficiently *scan titles, abstracts, and figures first. Sample about 10% of each paper to decide if it's worth diving into.*
04:35 Skip *sections liberally, focusing on abstracts, intros, and conclusions. Don't get discouraged if some parts are challenging; practice improves understanding.*
05:28 Regularly *reading papers builds a strong knowledge base, making you stand out in the long term. Consider it an investment in your career growth.*
06:21 Recommended *sources for finding research papers: Papers with Code, Distilled AI, Deep Learning Monitor, and Archive Sanity. Each has its pros and cons.*
Made with HARPA AI
Thanks for teaching us. These type of content is really rare in on YT.
Finally some real advice rather than nowadays learn dl/ml in a day. Awesome work, keep it up Jean.
Happy to help!
Do you think one should get completely comfortable with all the prerequisites of a method discussed in the paper? Sometimes there is too much of that (for a not-so-experienced reader)? What kind of code implementation details should you care about, if any, aside from reading the paper?
Sure thing! It's good to understand the basics of a method from a research paper, but you don't have to stress about every little detail, especially if you're not super experienced. Just focus on the main ideas first.
When it comes to the code part, try to get the overall picture rather than getting stuck in all the tiny code details. Later on, if you want to, you can dig into the code more if needed
Thanks for the amazing video
I'm glad you enjoyed it!
My Q is on implementing research papers as projects. Does that raise your value in the market? If yes, how would you recommend going about building a repository of research worthy of a valuable job?
Research papers raise your value if you get them published. I talked briefly about it here ua-cam.com/video/IzeVmfc73CA/v-deo.html
More on them coming in the next ai roadmap video. Stay tuned!
You’re the best!
Very helpful video ❤
Thank you for educating and inspiring us 🎉❤
Thank you so much for sharing these nifty tips!
You are so welcome!
Would you use similar steps in finding your own topic to research about for a dissertation?
How does one build familiarity with all the scary math notation?
Before reading ,i would highly recommend taking a free course on ML mathematics . But if you have an engineering degree you can skip those , its basically the things they taught you in the Uni (calculus 1,2,3 , probability,statistics,queuing theory etc).
Thanks
Welcome
I cant find 'distilled AI List of research papers', could anyone provide me link
Links to the sites referenced in the video are on our website www.exaltitude.io/job-seekers? which is also in the video descriptions
Mam how to do cold calling properly when searching for a job after a big career gap of 5 yrs due to family reasons ? I am an hvac engineer by profession trying to get back into work by looking for different job hunt methods..
This is a great question for our upcoming LinkedIn event. Please ask here and I’ll answer during the q&a Landing Dream Interviews: Mastering LinkedIn for Software Engineers
www.linkedin.com/events/masteringlinkedinforsoftwareeng7138971303499235328/
@@exaltitude Okay ✅
Day 1: of following you
Thanks for following!
Hey i love your videos ❤ but as a subscriber i wanna give you a suggestion your voice is too sharp please edit the audio just the cut off high pitches then it will be soothing while we listen to this as a podcast 😅😅 ,yeah i actually listen to and learn from you guys while coding 😊
Thanks for the suggestion. I do all the editing myself so I’ll keep take it into consideration. Out of curiosity what device are you watching from? I noticed the sound quality is very different on my iPhone vs computer.
@@exaltitudeon my iPhone 🙂
I disagree. I am also listening from iPhone and she sounds normal. It might just be you, individually.
i love you.
pretty mommy
bro