Machine Learning – Up As Pro

UpasPro, Pedram Agand personal blog. AI and financial advice

Unlocking faster AI performance is the focus of today’s post! Discover how block sparsity speeds up Vision Transformers (ViTs) by 1.46x with minimal accuracy loss, potentially benefiting large language models too. Learn about RAPIDS cuDF integration in Google Colab, offering up to 50x acceleration for pandas code on GPU instances. Plus, dive into the efficient implementation of Kolmogorov-Arnold Network (KAN) that reduces memory costs and enhances computation efficiency.

Code Machine Learning Series Technology

December 6, 2024 admin

Technow: Cost and run time to train GPT, RT-DETR, Tarsier, PyTorch’s “bottleneck”

This article covers some crucial AI advancements, from training costs to optimizing model efficiency. First, we explore the cost and

Academic Concept Machine Learning Series

November 22, 2024 admin

Deepdive: SimPO outperform DPO, Data Curation for Self-Supervised, Attention as an RNN

This post dives into three groundbreaking AI advancements designed to improve efficiency and scalability in model training and data processing including SimPO instead of Direct Preference Optimization (DPO), Meta’s Automatic Data Curation technique and hybrid attention-RNN module .

Academic Code Concept Machine Learning paper Series

November 15, 2024 admin

Deepdive: half memory with sequential backward calls, SaySelf, Diffusion On Syntax Trees

Unlock transformative advancements in AI with these three cutting-edge techniques. First, learn how to slash your GPU memory usage by up to 50% with a simple PyTorch trick, allowing you to double your batch size by calling backward() on each loss separately. Next, discover SaySelf, a revolutionary framework for Large Language Models (LLMs) that drastically improves confidence estimation by 30%, providing more reliable self-reflective rationales and reducing errors. Finally, dive into the world of neural diffusion models with a technique that edits syntax trees directly, boosting code generation efficiency by 20% and enhancing debugging accuracy. These innovations are poised to redefine AI performance, making your models faster, more efficient, and safer.

Academic Machine Learning Series Technology

November 8, 2024 admin

Technow: LLM Bootcamp, YOLOv10, Grokfast

Dive into the latest AI innovations that are transforming the landscape of machine learning and computer vision. First, explore the LLM Bootcamp by Full Stack Deep Learning, a comprehensive YouTube course that gets you up to speed on building and deploying cutting-edge language model applications. From prompt engineering and LLMOps to UX design and augmented models, this bootcamp covers everything you need to create state-of-the-art AI solutions. Next, discover YOLOv10, the latest in real-time object detection frameworks that boasts 46% less latency and 25% fewer parameters than its predecessors, making it perfect for high-speed applications like autonomous driving. Finally, accelerate your model’s learning process with Grokfast, an algorithm that speeds up grokking by up to 50 times, reducing the excessive iterations typically required for models to generalize. These advancements offer a powerful toolkit for anyone looking to push the boundaries of AI development.

Academic Code Machine Learning Series Technology

October 25, 2024 admin

Deepdive: Mind of LLM, Mamba-2, Dask

Anthropic has unveiled a groundbreaking paper that delves into the internal workings of a Large Language Model (LLM), offering unprecedented insights into the previously mysterious “black box” nature of these models. By employing a technique called “dictionary learning,” the research team successfully mapped the internal states of Claude 3 Sonnet, isolating patterns of neuron activations and representing complex model states with fewer active features. This innovative approach revealed a conceptual map within the model, showing how features related to similar concepts, such as “inner conflict,” cluster together. Even more astonishing, the researchers found that by manipulating these features, they could alter the model’s behavior—an advancement with significant implications for AI safety. This study represents a major leap in understanding and potentially controlling LLMs, though challenges remain in fully mapping and leveraging these features for practical safety applications.

Academic Code Machine Learning News Technology

October 18, 2024 admin

Technow: Context Managers Using contextlib, Phi-3 family, Verba RAG

Learn how Python’s contextlib module simplifies resource management with the with statement; Microsoft’s latest strides in the small language model race with the Phi-3 family, multimodal model and Copilot+ PCs; Copilots now support team collaboration and customizable AI agents for complex business processes; Verba RAG, Weaviate’s open-source tool for Retrieval-Augmented Generation, offering a user-friendly interface and versatile deployment options for advanced text generation tasks.

Algorithm FEATURED Machine Learning paper Series

October 11, 2024 admin

Deep dive: Llama3 from scratch, LinearBoost, LoRA Learns and Forgets Less

In this post, we’ll explore three groundbreaking advancements that are pushing the boundaries of AI and machine learning. First, dive into the intricacies of LLaMa 3, implemented from scratch in Python, where every aspect, from attention mechanisms to tokenization, is meticulously explained, making it a must-see for anyone interested in model architecture. Next, discover how LinearBoost, a new linear classifier-based algorithm, outperforms traditional GBDTs like CatBoost and XGBoost, showcasing superior accuracy and response time across five benchmark datasets. Lastly, we’ll delve into the debate on Low-Rank Adaptation (LoRA) in fine-tuning large language models, revealing why LoRA might not match full fine-tuning in specialized domains but offers remarkable regularization benefits. These insights are not only educational but also essential for staying at the forefront of AI research and application.

Concept Machine Learning

October 4, 2024 admin

Large Model Checkpointing

There are various aspects to optimize when training large models. It often lasts weeks and involves managing billions of rows

Academic Concept Machine Learning paper

September 27, 2024 admin

Are Models Converging Towards the Same Representation of the World?

Are AI models converging in the way they represent the world? This recent paper says yes. The authors analyze several language and vision models, their learned latent spaces and the way they measure distances between data points, and conclude that there is a noticeable and growing alignment between all of them.