Top Data Validation Tools for Machine Learning
Author(s): Eryk Lewinson Originally published on Towards AI. Discover Python tools that can catch any issues with your data!Image generated with Midjourney It was challenging to stop myself from starting this article with some variation of the popular phrase “garbage in, garbage …
Let’s Talk Auto-Encoders
Author(s): Aminul Huq Originally published on Towards AI. In the field of deep learning, auto-encoders play a vital role. They have been used for various tasks, such as image reconstruction, noise removal, encoding, etc. Some people also use them in innovative ways …
Understanding MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
Author(s): Hesam Sheikh Originally published on Towards AI. the math and intuition behind a novel parameter-efficient fine-tuning methodThe outline of MoRA vs LoRA. (source: MoRA paper) A recent, “MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning”, introduces a new method into the family of …
LoRA Learns Less and Forgets Less
Author(s): Hesam Sheikh Originally published on Towards AI. We will go through LoRA (Low-Rank Adaptation of Large Language Models), what it is, and the interesting properties of LoRA when compared to Full Fine-TuningLoRA from the original paper. LoRA is one of the …
Building AI Agents With Crew AI using Google Gemini, Groq, LLama3
Author(s): Suhaib Arshad Originally published on Towards AI. Inspired from: Enable AGI | How to Create Autonomous AI Agents with GPT-4 & Auto-GPT — YouTube Introduction In the recent uproar of Devin AI’s emergence, there was a genuine concern in the market …
Top Important LLMs Papers for the Week from 27/05 to 02/06
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Large Language Models Research Large language models (LLMs) have advanced rapidly in recent years. As new generations of models are developed, researchers and engineers need to stay informed on the …
Automatic Trend Change Points Detection in Time Series Analysis
Author(s): Daniel Pollak Originally published on Towards AI. Photo by rc.xyz NFT gallery on Unsplash Lately, I’ve been extensively involved in analyzing high-frequency time series characterized by linear trends. My focus has been on developing a dependable and simple forecasting model, based …
Reduce Risks when Coding with AI, AI Consulting Opportunities, Mistral 7B Deep Dive #27
Author(s): Towards AI Editorial Team Originally published on Towards AI. Master the art of building LLMs with our 470+ page guide! Good morning, AI enthusiasts! The last couple of weeks have been super busy with some really interesting launches, like our book …
Top Important Computer Vision Papers for the Week from 27/05 to 02/06
Author(s): Youssef Hosni Originally published on Towards AI. Stay Updated with Recent Computer Vision Research Every week, researchers from top research labs, companies, and universities publish exciting breakthroughs in various topics such as diffusion models, vision language models, image editing and generation, …
Artificial General Ignorance and AI Bubble
Author(s): Fabio Matricardi Originally published on Towards AI. Overcome our own biases and start from ABC: a hard, inevitable path.image by the author and lexica.art Have you ever stopped to consider just how much you think you know about Artificial Intelligence? In …
Deepspeed ZeRO-DP: distributed training for large models
Author(s): Amina Shabbeer Originally published on Towards AI. Deepspeed’s ZeRO (Zero Redundancy Optimizer) is a distributed training framework with a number of optimizations to easily train large deep learning models across multiple GPUs and nodes. These optimizations reduce memory redundancy and communication …
Feature Selection and Generalization using Regularization
Author(s): Shahriar Hossain Originally published on Towards AI. An Overview of L1 and L2 Regularization Techniques and a Case Study on Feature Selection Using Neural NetworksSource: Image by the author. The image was drawn using Canva. Overfitting is a common challenge in …
Kolmogorov-Arnold Networks for Mathematical Discovery
Author(s): Shenggang Li Originally published on Towards AI. Exploring Prime Number Distribution and Predictive Modeling with KAN and Other Mathematical InsightsPhoto by Saad Ahmad on Unsplash In my previous paper, Unpacking Kolmogorov-Arnold Networks, I introduced the mechanism of KAN, a novel framework …
Here’s How to Create a Bar Chart Race in Minutes for Any Data
Author(s): Muhammad Saad Uddin Originally published on Towards AI. Before diving into this article, let me share a little story with you. I had two potential titles for this piece: “Building Dynamic Bar Chart Races for LLM Leaderboards in Python” and “Here’s …
How To Use Target Encoding in Machine Learning Credit Risk Models — Part 2
Author(s): Varun Nakra Originally published on Towards AI. In my previous story, Part 1 of this topic — https://medium.com/towards-artificial-intelligence/how-to-use-target-encoding-in-machine-learning-credit-risk-models-part-1-cf72f30f51a8 We covered the derivation of the expression of WoE using maximum likelihood. Now, we will apply it practically on a random dataset. import …
Popular posts
Updates
Recent Posts
