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New Book: Statistical Optimization for Generative AI and Machine Learning. With case studies, Python code, new open source libraries, and applications of the GenAI game-changer technology known as NoGAN (194 pages). By Dr. Vincent Granville, one of the world leaders in AI and machine learning. Get your copy at https://lnkd.in/gdFKcM2G

The New Generation of RAG and LLM Architectures: Access all the material at https://lnkd.in/g58xUvg8 In particular, my high-level PowerPoint presentation on the topic, featuring multi-tokens, contextual tokens, LLM routers, evaluation metrics used as adaptive loss function for better results, enterprise LLMs, fast-tuning like LoRA, auto-tuning, mixture of experts, knowledge graphs, LLM for search / clustering / predictive analytics, LLM with no transformer, variable-length embeddings, and alternative to vector and graph databases. 

New Book: State of the Art in GenAI & LLMs — Creative Projects, with Solutions https://lnkd.in/gxEWNNEw With 23 top projects, 96 subprojects, and 6000 lines of Python code, this vendor-neutral coursebook is a goldmine for any analytic professional or AI/ML engineer interested in developing superior GenAI or LLM enterprise apps using ground-breaking technology. This is not another book discussing the same topics that you learn in bootcamps, college classes, Coursera, or at work. Instead, the focus is on implementing solutions that address and fix the main problems encountered in current applications. Using foundational redesign rather than patches such as prompt engineering to fix backend design flaws. You will learn how to quickly implement from scratch applications actually used by Fortune 100 companies, outperforming OpenAI and the likes by several order of magnitudes, in terms of quality, speed, memory requirements, costs, interpretability (explainable AI), security, latency, and training complexity.

GenAI Evaluation Metrics: Your Best Loss Functions to Boost Quality https://lnkd.in/gyUbtS8e Whether dealing with LLM, computer vision, clustering, predictive analytics, synthetization, or any other AI problem, the goal is to deliver high quality results in as little time as possible. Typically, you assess the output quality after producing the results, using model evaluation metrics. These metrics are also used to compare various models, or to measure improvement over the baseline. In unsupervised learning such as LLM or clustering, evaluation is not trivial. But in many cases, the task is straightforward. Yet you need to choose the best possible metric for quality assessment. Otherwise, it results in bad output rated as good. The best evaluation metrics may be hard to implement and compute. At the same time, pretty much all modern techniques rely on minimizing a loss function to achieve good performance. In particular, all neural networks are massive gradient descent algorithms that aim at minimizing a loss function. The loss function is usually basic (for instance, sums of squared differences) because it must be updated extremely fast each time a neuron gets activated and a weight is modified. There may be trillions of changes needed before getting a stable solution. In practice, the loss function is a proxy to the model evaluation metric: the lower the loss, the better the evaluation. At least, that’s the expectation [..] Continue reading, get the code, and see how to not get stuck in a local optimum: ➡️ https://lnkd.in/gyUbtS8e

Breakthrough: Zero-Weight LLM for Accurate Predictions and High-Performance Clustering https://lnkd.in/gVEAiJGE While most AI companies keep building LLMs with more weights and tokens (now one trillion is a standard number), I went in the opposite direction. Of course, zero weight means that there is no neural network behind the scenes. More specifically, it means that there is no lengthy Blackbox process to find the “best” weights optimizing a loss function. In reality, weights are still present, very much like in a neural network, but they are explicitly specified. Indeed, I use parametric weights, governed by a few explainable parameters. The optimization focuses on these few parameters, and reduces overfitting. It has similarities to regularization methods, where weights are highly constrained to better control the outcome and interpretation of the results. I implemented similar techniques in the past with xLLM. However, in this new application, I made the core formula very straightforward and prominent in my article to help you make the connection with deep neural networks, find the analogy, and see the exact point where and how both approaches start diverging.

Evaluate and Build High Performance Taxonomy-Based LLMs From Scratch https://lnkd.in/gY8rz79X Learn how to design better LLMs from scratch by: o Creating a taxonomy from scratch based on a crawled corpus, in a semi-automated way. o Using an external taxonomy that covers your specific domain: one for each specialized sub-LLM. This process is fully automated. o Evaluating LLMs by reconstructing the corpus taxonomy, and comparing it with the real one. Open-source code and full documentation included, with use case and illustrations. Some of the features of xLLM: - Muliple specialized LLMs (2000 for entire human knowledge) - User can choose hyperparameters and damain area (sub-LLM) - Customized and self-tuned - No latency, local or secure enterprise version possible - x-embeddings with words (multi-token) rather than tokens - Variable length embeddings and x-embeddings - No hallucination - Returns links to input sources and related concepts - Based on structure of underlying corpus, not just words - Fast approximate nearest neigbhor search - No neural network, no GPU, no training: fast, inexpensive - Small backend tables, 30k multi-tokens and 5k tokens per sub-LLM - Very easy to optimize - No prompt engineering needed

Hallucination-Free, Self-Tuned, Fast Hierarchical LLMs with Multi-Token Embeddings https://lnkd.in/gCkvne-K

How the New Breed of LLMs is Replacing OpenAI and the Likes https://lnkd.in/gBKfxiGV Of course, OpenAI, Mistral, Claude and the likes may adapt. But will they manage to stay competitive in this evolving market? Last week Databricks launched DBRX. It clearly shows the new trend: specialization, lightweight, combining multiple LLMs, enterprise-oriented, and better results at a fraction of the cost. Monolithic solutions where you pay by the token encourage the proliferation of models with billions or trillions of tokens, weights and parameters. They are embraced by companies such as Nvidia, because they use a lot of GPU and make chip producers wealthy. One of the drawbacks is the cost incurred by the customer, with no guarantee of positive ROI. The quality may also suffer (hallucinations). In this article, I discuss the new type of architecture under development. Hallucination-free, they achieve better results at a fraction of the cost and run much faster. Sometimes without GPU, sometimes without training. Targeting professional users rather than the layman, they rely on self-tuning and customization. Indeed, there is no universal evaluation metric: laymen and experts have very different ratings and expectations when using these tools. Much of this discussion is based on the technology that I develop for a fortune 100 company. I show the benefits, but also potential issues. Many of my competitors are moving in the same direction.

7 GenAI & ML Concepts Explained in 1-Min Data Videos https://lnkd.in/g3EXrGtq Not your typical videos: it’s not someone talking, it’s the data itself that “talks”. More precisely, data animations that serve as 60-seconds tutorials. I selected them among those that I created in Python and posted on YouTube. Each frame represents a new data or training set (real or synthetic), a different model in a particular family, different parameters or hyperparameters, or a new iteration in some evolving system. The videos consist of hundreds of frames, with between 4 and 20 frames per second. For detailed explanations and Python code, see “source” below each video when reading the article.

GenAItechLab Fellowship https://lnkd.in/g7xzy7aJ The GenAItechLab Fellowship program allows participants to work on state-of-the-art, enterprise-grade projects, entirely for free, at their own pace, at home or in their workplace. The goal is to help you test, enhance, and further implement applications that outperform solutions offered by AI startups or organizations such as Google or OpenAI.