Learn AI Together — Towards AI Community Newsletter #13

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, fellow AI enthusiasts! In this week’s edition of the Learn AI Together newsletter, we have a comprehensive guide designed to teach everything about large language models (LLMs) in 2024 for free. ‘From zero to hero with LLMs’ is a curated collection of resources to make cutting-edge AI knowledge accessible to all with the wealth of free online materials. We hope you find it useful.

We also wanted to learn more about how many of you are working in the industry, a handy poll for those looking to find the best deal possible. More details in this iteration!

I wish you all a great read and an amazing weekend!

What’s AI Weekly

Louis-François Bouchard has compiled LLM resources as a complete guide to starting and improving your LLM skills in 2024 without an advanced background in the field. It is intended for anyone with a small programming and machine learning background. There is no specific order, but a classic path would be from top to bottom. All resources listed here are free, except some online courses and books, which are recommended for a better understanding. Still, it is possible to become an expert without them, with more time spent on online readings, videos, and practice. Find the ‘From zero to hero with LLMs’ guide here!

• Louis-François Bouchard, Towards AI Co-founder & Head of Community

Learn AI Together Community section!

Featured Community post from the Discord

Ryios shared an exciting new idea with the community: instead of 1 mega model, one good general language model feeds down into hundreds, thousands, or even millions of smaller micro models that specialize in something. Mega Models are too expensive to train, host, and run inference. They exceed the capabilities of most consumer hardware. To address this, instead of training a model to know everything, ryios proposes training a model to be well-versed in the language. This AI’s task is to translate user prompts into formats that can be understood by other AIs downstream, much like a translator or secretary. Join the conversation and share your thoughts in the thread!

AI poll of the week!

While Google Cloud seems the preferred choice, the thread is flooding with recommendations for Digital Ocean, Oracle, AWS, and more. Let us know your favorites in the thread!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Mh_aghajany is looking for fellow learners to explore Machine Learning, Deep Learning, and LLM. If you’re passionate about ML and interested in collaborative learning, connect in the thread!

2. Our friends at Zoī are hiring their Chief AI Officer. Zoī is at the crossroads of 3 domains: Medical, Data Science, and BeSci. Zoī aims to create personalized user manuals for each member by gathering only the necessary data to provide tailored recommendations based on thousands of factors. Find more information in the thread!

3. Usmanyousaaf is looking for a study partner to dive into ANN, CNN, RNN, LSTM, GRU, Transformers, pre-trained models, GANs, and more. If you are also learning the math behind each and want to work on projects, reach out in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

Advanced RAG 04: Re-ranking by Florian June

This article introduces RAG’s re-ranking technique and demonstrates how to incorporate re-ranking functionality using two methods. Re-ranking is crucial in the Retrieval Augmented Generation (RAG) process. In a naive RAG approach, a large number of contexts may be retrieved, but not all are necessarily relevant to the question. Re-ranking allows for the reordering and filtering of documents, placing the relevant ones at the forefront, thereby enhancing the effectiveness of RAG.

Our must-read articles

1. easy-explain: Explainable AI with GradCam by Stavros Theocharis

GradCam is a widely used Explainable AI method that has been extensively discussed in forums and literature. Therefore, the author has included this common method in his package, “easy-explain,” but in an abstract way so that anyone can use it easily. The article walks you through it.

2. LangChain 101: Part 3b. Talking to Documents: Embeddings and Vectorstores by Ivan Reznikov

In this part of the LangChain 101 series, the author discusses what embeddings are and how to choose one, what vector stores are, how vector databases differ from other databases, and, most importantly, how to choose one! All code is provided and duplicated in Github and Google Colab.

3. Lumiere, Google’s Amazing Video Breakthrough by Ignacio de Gregorio

Google has taken us one step closer, as their approach to AI video synthesis is not only revolutionary but also showcases incredible video quality and a wide range of amazing skills like video in/outpainting, image animation, and video styling, making it the new reference in the field.

4. Understanding the Mechanics of Neural Machine Translation by Saif Ali Kheraj

As large language models become more prevalent, we must study and concentrate on attention models, which play an essential role in both Transformer and language models. First, it is a good idea to understand the Sequence to Sequence Encoder Decoder Network. After that, proceeding to the most important “Attention Model” and examine it in greater detail.

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