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rs at mid-market companies that do a few hundred million of revenue and need to increase their margin profile, please just go into AI consulting. You'll bootstrap to 100s of Ms if you stick with it

At the core of the attention mechanism in LLMs are three matrices: Query, Key, and Value. These matrices are how transformers actually pay attention to different parts of the input. In this write-up, we will go through the construction of these matrices from the ground up.

Deep Learning with Python by François Chollet (5:15) Getting Started with Natural Language Processing (6:46) The 100-Page Language Models (8:04) Linear Algebra Done Right (9:20)

Further reading:

This course is also available on my website and as an ebook on leanpub. Please leave a ⭐ as motivation if this was helpful!

Bidirectional Encoder Representations from Transformers (BERT) is a Large Language Model (LLM) developed by Google AI Language which has made significant advancements in the field of Natural Language Processing (NLP). Many models in recent years have been inspired by or are direct improvements to BERT, such as RoBERTa, ALBERT, and DistilBERT to name a few. The original BERT model was released shortly after OpenAI’s Generative Pre-trained Transformer (GPT), with both building on the work of the Transformer architecture proposed the year prior. While GPT focused on Natural Language Generation (NLG), BERT prioritised Natural Language Understanding (NLU). These two developments reshaped the landscape of NLP, cementing themselves as notable milestones in the progression of machine learning.

To go one level deeper on LPUs versus GPUs (the processors most LLMs run on, typically Nvidia cards): those GPU calculations have to access a lot of things in memory. Nvidia’s chips depend on HBM, high bandwidth memory. But LPUs use something called SRAM that’s much faster to reference.

new blogs [1][2][3] on ODEs and SDEs in machine learning.01/23/25Added bibfile for book andLaTeX for all equations12/17/24Video lectures for chapters 1-12 from Tamer Elsayed of Qatar University.12/05/24New blog on Neural network Gaussian processes11/14/24New blog on Bayesian Neural Networks08/13/24New blog on Bayesian machine learning (function perspective)Show moreCITATION: @book{prince2023understanding, author = "Simon J.D. Prince", title = "Understanding Deep Learning", publisher = "The MIT Press", year = 2023, url = "http://udlbook.com" } Follow me on Twitter or LinkedIn for updates.