With the significant progress of large reasoning models in complex coding and reasoning tasks, existing benchmarks, like LiveCodeBench and CodeElo, are insufficient to evaluate the coding capabilities of large language models (LLMs) in real competition environments. Moreover, current evaluation metrics such as Pass@K fail to capture the reflective abilities of reasoning models. To address these challenges, we propose \textbf{ICPC-Eval}, a top-level competitive coding benchmark designed to probing the frontiers of LLM reasoning. ICPC-Eval includes 118 carefully curated problems from 11 recent ICPC contests held in various regions of the world, offering three key contributions: 1) A challenging realistic ICPC competition scenario, featuring a problem type and difficulty distribution consistent with actual contests. 2) A robust test case generation method and a corresponding local evaluation toolkit, enabling efficient and accurate local evaluation. 3) An effective test-time scaling evaluation metric, Refine@K, which allows iterative repair of solutions based on execution feedback. The results underscore the significant challenge in evaluating complex reasoning abilities: top-tier reasoning models like DeepSeek-R1 often rely on multi-turn code feedback to fully unlock their in-context reasoning potential when compared to non-reasoning counterparts. Furthermore, despite recent advancements in code generation, these models still lag behind top-performing human teams. We release the benchmark at: https://github.com/RUCAIBox/Slow_Thinking_with_LLMs

Speed up your daily workflows by getting AI to write Python code in seconds. On March 14, 2023, OpenAI launched GPT-4, the newest and most powerful version of their language model. Within just hours of its launch, GPT-4 stunned people by turning a hand-drawn sketch into a functional website, passing the bar exam, and generating accurate summaries of Wikipedia articles. It also outperforms its predecessor, GPT-3.5, in solving math problems and answering questions based on logic and reasoning. ChatGPT, the chatbot which was built on top of GPT-3.5 and released to the public, was notorious for "hallucinating."

Linear regression is a statistical technique for estimating the relationship between two variables. A simple example of linear regression is to predict the height of someone based on the square root of the person’s weight (that’s what BMI is based on). To do this, we need to find the slope and intercept of the line. […]

Udemy is the biggest website in the world that offer courses in many categories, all the skills that you would be looking for are offered in Udemy, including languages, design, marketing and a lot of other categories, so when you ever want to buy a courses and pay for a new skills, Udemy would be the best forum for you. You can find payment courses, 100 free courses and coupons also, more than 12 categories are offered, and that what makes sure you will find the domain and the skill you are looking for. Our duty is to search for 100 off courses and free coupons. AI is an enabler in transforming diverse realms by exploiting deep learning architectures. The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the Generative Adversarial Networks used for data creation in deep learning routines.

Before we see how the tf.data API works, let's review how we usually train a Keras model. First, we need a dataset. An example is the fashion MNIST dataset that comes with the Keras API, which we have 60,000 training samples and 10,000 test samples of 28 28 pixels in grayscale and the corresponding classification label is encoded with integers 0 to 9. The dataset is a NumPy array. Then we can build a Keras model for classification, and with the model's fit() function, we provide the NumPy array as data.

AI is an enabler in transforming diverse realms by exploiting deep learning architectures. The course aims to expose students to cutting-edge algorithms, techniques, and codes related to AI and particularly the Generative Adversarial Networks used for data creation in deep learning routines.

So, I got access to the technical preview of Github Copilot, and I was excited to test it out. I decided to test it out in various languages and see how helpful it is! It is a very excellent tool which is the best autocomplete functionality I have ever seen. It makes writing code more effortless and a lot faster by having to type less code. It is beneficial in cases when you are learning a new language or a new framework.

Chatbots are a powerful way to provide conversational experiences for any software product. Each conversational experience depends on the implementation of the chatbot to either be a good or poor experience for the end user. The modern day world is living in the technology wave of Artificial Intelligence and bots are a huge part of it. In this tutorial, we are going to build a chatbot application from scratch using Dialogflow and React Native. The main reason to use Google's Dialogflow for this tutorial is that you do not have to go through a hefty signup process by providing your card details, unlike other bot frameworks or similar service providers.

Recently, I actively started looking for a job change to Data science, I don't have any formal education like Masters or Ph.D. background in AI/Machine Learning. I started learning it completely out of my own interest (not just because of hype). It was one of the challenging tracks to opt-in especially if you are working simultaneously on some other technology. I started my journey by enrolling myself in many MOOCs(Massive Open Online Courses) and started reading multiple blogs. It slowly started making sense.

This tutorial demonstrates how to classify structured data (e.g. We will use Keras to define the model, and feature columns as a bridge to map from columns in a CSV to features used to train the model. We will use a simplified version of the PetFinder dataset. There are several thousand rows in the CSV. Each row describes a pet, and each column describes an attribute.