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I've been told by people in the software field that python is particularly good for dealing with big amounts of data.It's a good bet. Personally i never liked Python, mostly because of syntax, but it could just be me. I want to give it a shot. My first preference was Rust but i'll think Python is a good bet.
I've been told by people in the software field that python is particularly good for dealing with big amounts of data.
Perhaps i am not the right person to judge, because i have never used Python, but i really doubt Python is better at dealing with big amounts of data in comparison with lower-level languages like C++, Rust, Golang (on terms of time and space performance). I am 100% convinced on this. The difference is that Python is a higher-level language and has a lot of available ready made modules/scripts which help achieve the goals. And when put into a tradeoff of how much computing/memory power it consumes, i guess it's more reasonable to go with Python due to sheer availability of modules and scripts and not so much difference on terms of computing power due to recent spike off of having more and more powerful computers and having more memory available which one need not to worry about and hence why using a dynamic and scripting language like Python which achieves the same goal with less code is more convenient than using a compiled and more strictly typed languages like C++ or Java which development might be slower than a dynamic typing language like Python.
I will definitely delve into it. I am usually into JavaScript, but expanding into Python as well might be a very good bet. At the end of the day all programming languages share the same paradigms and very similar syntax, they differ on their approach. Hence mastering the core computing skills like algorithms, data structures, problem-solving skills, some skills in discrete math will be crucial. AI and Machine learning has Statistics and Probability as crucial part of their curriculum.
Thanks for the insights, I figure if I tinker with it, and look at some tutorials on YouTube; I'll eventually get the hang of it.
Nevertheless, soon, there will be AI plugins that will help people write code more perfectly. But understanding how it works from a coding language perspective would allow for better understanding of how to prompt the AI to do what you want.
My friend that works in tech, downloaded Visual Studio Code on my PC for me to play around with so I could learn.
For those interested, there is a short and free course (for now) on the deeplearning.ai website that teaches best practices for developing prompts for ChatGPT through the API (Application Program Interface). It is specifically for programmers and requires some programming knowledge, namely Python, but anyone interested in the subject can learn. One of the things they teach is how to build a custom chatbot.
Course presentation
https://www.deeplearning.ai/the-batch/new-course-chatgpt-prompt-engineering-for-developers/
Course link
https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
Good one. Thanks for sharing.
Take a look at this:
https://medium.com/javascript-scene...am-prompt-engineering-by-example-f7a7b65263bc
https://medium.com/javascript-scene/the-art-of-effortless-programming-3e1860abe1d3
I practiced in GPT-4 and i was impressed by the potential.
Below is a list of the top 2-way combinations for genetic distances to Jovialis using ancient DNA samples from Antonio et al. 2019. This was done using Vahaduo and Dodecad K12b format. After reading the assigned reference PDFs in the links below, develop a theory on the genetic origins of Jovialis citing the studies. Jovialis' background is that all four of his grand parents, going back as far as they can remember come from the same two towns in the region of Bari, Italy.
Read these PDFs for reference:
Antonio et al. 2019: doc_id: 35b6f07e-0a03-4f97-a299-df4d20fec4ea
Antonio et al. 2019 Supplement: doc_id: 83527de9-d8ab-42fc-8123-96ea3ff7c31d
Raveane et al. 2019: doc_id: 6e372aab-ebc2-4cca-bc67-54b6c1ccc2b3
Sarno et al. 2021: doc_id: 21194de6-f320-45a1-927e-c6de6373da73
Distance to: | Jovialis |
---|---|
1.79968643 | 57.00% Latini_o_IA:R850:Antonio_2019 + 43.00% Etruscan_IA:R474:Antonio_2019 |
3.00380176 | 50.00% Latini_o_IA:R850:Antonio_2019 + 50.00% Protovillanovan_IA:R1:Antonio_2019 |
4.01287706 | 61.20% Protovillanovan_IA:R1:Antonio_2019 + 38.80% Anatolian_BA:I2683:Lazaridis_2017 |
4.03382623 | 56.60% Protovillanovan_IA:R1:Antonio_2019 + 43.40% Anatolian_BA:I2499:Lazaridis_2017 |
4.09031917 | 58.80% Protovillanovan_IA:R1:Antonio_2019 + 41.20% Anatolian_C:I1584:Lazaridis_2016 |
4.34112559 | 66.00% Latini_o_IA:R850:Antonio_2019 + 34.00% Latini_IA:R851:Antonio_2019 |
4.47220383 | 64.80% Latini_o_IA:R850:Antonio_2019 + 35.20% Latini_IA:R1021:Antonio_2019 |
4.79374307 | 57.40% Protovillanovan_IA:R1:Antonio_2019 + 42.60% Anatolian_BA:I2495:Lazaridis_2017 |
4.86450214 | 49.40% Etruscan_IA:R474:Antonio_2019 + 50.60% Anatolian_BA:I2499:Lazaridis_2017 |
4.88968486 | 63.40% Latini_o_IA:R850:Antonio_2019 + 36.60% Etruscan_IA:R473:Antonio_2019 |
4.97616374 | 51.80% Etruscan_IA:R474:Antonio_2019 + 48.20% Anatolian_C:I1584:Lazaridis_2016 |
5.15944684 | 62.60% Latini_o_IA:R850:Antonio_2019 + 37.40% Latini_IA:R1016:Antonio_2019 |
5.48804815 | 54.40% Etruscan_IA:R474:Antonio_2019 + 45.60% Anatolian_BA:I2683:Lazaridis_2017 |
6.00699384 | 50.00% Etruscan_IA:R474:Antonio_2019 + 50.00% Anatolian_BA:I2495:Lazaridis_2017 |
7.88534546 | 63.80% Protovillanovan_IA:R1:Antonio_2019 + 36.20% Minoan_Odigitria:I9131:Lazaridis_2017 |
7.94697556 | 65.00% Protovillanovan_IA:R1:Antonio_2019 + 35.00% Minoan_Petras_EBAta08:Clemente_2021 |
8.38214365 | 67.40% Protovillanovan_IA:R1:Antonio_2019 + 32.60% Minoan_Lasithi:I0073:Lazaridis_2017 |
8.39326505 | 64.40% Protovillanovan_IA:R1:Antonio_2019 + 35.60% Minoan_Lasithi:I9005:Lazaridis_2017 |
8.50505050 | 48.60% Protovillanovan_IA:R1:Antonio_2019 + 51.40% Mycenaean:I9041:Lazaridis_2017 |
8.59873033 | 60.40% Protovillanovan_IA:R1:Antonio_2019 + 39.60% Mycenaean:I9006:Lazaridis_2017 |
8.60386203 | 66.40% Protovillanovan_IA:R1:Antonio_2019 + 33.60% Minoan_Lasithi:I0071:Lazaridis_2017 |
8.72487111 | 68.20% Protovillanovan_IA:R1:Antonio_2019 + 31.80% Minoan_Lasithi:I0070:Lazaridis_2017 |
8.76645953 | 45.00% Etruscan_IA:R473:Antonio_2019 + 55.00% Anatolian_C:I1584:Lazaridis_2016 |
9.00946167 | 42.20% Etruscan_IA:R473:Antonio_2019 + 57.80% Anatolian_BA:I2499:Lazaridis_2017 |
9.06691271 | 39.00% Latini_IA:R851:Antonio_2019 + 61.00% Anatolian_BA:I2499:Lazaridis_2017 |
^^Maybe C6 represents the Proto-villanovan + EBA Anatolia_BA/ChL-like people that went on to become like R437 in the Iron Age. But we can see there was also similar ancestry from the Iron Age with R850, who was probably a hold over from the EBA genetic profile, mixing with Latins/Etruscans.
This one Proto-villanoan sample is similar to Croatia_IA, as well as Cetina_BA.
Nobody is denying that.
As for if Etruscans come from Proto-villanoan, it is not like R1 is radically different from Etruscans though.
ChatwithPDF is the best PDF reading tool I've used thus far. If you ask the web browser on chatgpt to look up studies, it will run into access issues. But if enable the plugin ChatwithPDF, and ask it to provide you a link to upload the PDF, it will host it for an hour, and you can ask ChatGPT to read it. I used it for work and it saved me probably 10 hours of BS. I highly recommend it, and I will use it going forward for papers.
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