Step 1.
Machine Learning and Deep Learning, have several topics to begin research in. Make sure that you choose one topic to get started with.

Step 2.
Use websites like paperswithcode.com to find topics relevant to your choice of subject and research.

Step 3.
It is important to gain a broader understanding of the paper. And this can be done by taking a closer look at the Title, Abstract, and then finally the Conclusion.

Step 4.
Read the content of your paper, and, find out the mandatory knowledge that you need to understand the content of your paper.

Step 5.
Thoroughly reading the research paper.
Grab a notebook and begin reading.

Step 6. Finding external resource.
In our final step, we need to explore all the terminologies, concepts, methodologies, and methods we noted down from our previous step.,

So go ahead and start reading that research paper that you have been putting out for so long. All the best!

For more tutorials on Data Science visit: https://www.jovian.com/

Original post → http://bit.ly/2D4Jyd1

Today, Melanie brings you another great interview from her time at Deep Learning Indaba in South Africa. She was joined by Yabebal Fantaye and Jessica Phalafala for an in-depth look at the deep learning research that’s going on in the continent.

At the African Institute for Mathematical Sciences, the aim is to gather together minds from all over Africa and the world to not only learn but to use their distinct perspectives to contribute to research that furthers the sciences. Our guests are both part of this initiative, using their specialized skills to expand the abilities of the group and stretch the boundaries of machine learning, mathematics, and other sciences.

Yabebal elaborates on the importance of AIMS and Deep Learning Indaba, noting that the more people can connect with each other, the more confidence they will gain. Jessica points out how this research in Africa can do more than just advance science. By focusing on African problems and solutions, machine learning research can help increase the GDP and economic standards of a continent thought to be “behind”.

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Currently my preferred LLM: FLAN-T5. Watch my code optimization and examples.
Released Nov 2022 – it is an enhanced version of T5. Great for few-shot learning.

(By the way, I had to provide my mail and telephone number to OpenAI for a preview test of ChatGPT, only during the research preview, usage of ChatGPT is free. Plus: ChatGPT is currently not available on HuggingFace. And you know … proprietary AI …)

We directly use /code FLAN-T5 for inference from Huggingface, without further fine-tuning the models weights.

All rights with:
https://huggingface.co/docs/transformers/main/en/model_doc/flan-t5
https://arxiv.org/pdf/2210.11416.pdf

#machinelearning
#ai
#datascience

Reality behind data science jobs. Is machine learning really cool?

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