Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text and static imagery alone can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focusing on text or static image inputs. To bridge this gap, we introduce VideoWebArena (VideoWA), a benchmark for evaluating the capabilities of long-context multimodal agents for video understanding. VideoWA consists of 2,021 web agent tasks based on manually crafted video tutorials, which total almost four hours of content. For our benchmark, we define a taxonomy of long-context video-based agent tasks with two main areas of focus: skill retention and factual retention. While skill retention tasks evaluate whether an agent can use a given human demonstration to complete a task efficiently, the factual retention task evaluates whether an agent can retrieve instruction-relevant information from a video to complete a task. We find that the best model achieves 13.3% success on factual retention tasks and 45.8% on factual retention QA pairs, far below human performance at 73.9% and 79.3%, respectively. On skill retention tasks, long-context models perform worse with tutorials than without, exhibiting a 5% performance decrease in WebArena tasks and a 10.3% decrease in VisualWebArena tasks. Our work highlights the need to improve the agentic abilities of long-context multimodal models and provides a testbed for future development with long-context video agents.

Python is one of the fastest-growing programming languages. It is widely used in various business sectors, such as programming, web development, machine learning, and data science. It is a high-level, object-oriented programming language with built-in data structures and dynamic semantics. Python supports different modules and packages, which allows program modularity and code reuse. The language has become so popular in recent times that aspirants are flocking to learn the language and acquire programming skills.

This course is aimed at beginners who have never programmed before, as well as existing programmers who want to increase their career options by learning Python. After completing the python beginner level please have a look in to advanced training'Python for professionals with real time examples -2022'. The fact is, Python is one of the most popular programming languages in the world – Huge companies like Google use it in mission-critical applications like Google Search. And Python is the number one language choice for machine learning, data science, and artificial intelligence. To get those high-paying jobs you need expert knowledge of Python, and that's what you will get from this course.

If you are a student or a professional looking for various open-source Natural Language Processing (NLP) projects, then, this article is made to help you. The NLP projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Text Summarizer is a project that can summarize long paragraphs of text into a single line summary. It can turn an article into a summary using Python and Keras library.

Machine Learning with Earth Engine Python and Colab Become an expert in machine learning, python, big geospatial data & land use land cover in google earth engine What you'll learn Description Welcome to the Machine Learning with Earth Engine Python and Colab course. This Earth Engine course is without a doubt the most comprehensive course for anyone who wants to apply machine learning in python using satellite data. Even if you have zero programming experience, this course will take you from beginner to mastery. The course includes HD video tutorials. We'll take you step-by-step through engaging video tutorials and teach you everything you need to know to apply remote sensing and cloud computing for forest monitoring application.

Blog - New features of Python 3.8 Sat, 30 Nov 2019 In this blog, I show how to use new features of Python 3.8. Video Tutorial - Upcasting and Downcasting in Java Wed, 27 Nov 2019 In this video, I explain upcasting and downcasting in Java Video Tutorial - How to use Lamdba Expressions in Java Sun, 24 Nov 2019 In this video, I demonstrate how to use Lambda Expression, Lambda Blocks, and Method Reference in Java 8 and above. Video Tutorial - Top-N Analysis in Oracle Database Tue, 19 Nov 2019 In this video, I show how to perform Top-N Analysis in Oracle Database 11g and 18c. Video Tutorial - Why to override equals(), hashCode() and toString() methods of Object class in Java. Sat, 16 Nov 2019 In this video, I explain why to overriding equals(), hashCode() and toString() methods of Object class in Java.

The Rubik's Cube has been around for decades. I've toyed with the cube, probably in the very late '80s or early '90s, but never even imagined being able to solve one; from entirely shuffled, to perfectly ordered. But wouldn't it be satisfying if I could? Fortunately, the internet makes solving what was originally an architecture puzzle, doable for most of us. The world record for solving a cube has plummeted since 2000 from 20 seconds to under five, as pros and enthusiasts synthesized high-speed solutions and turn combinations (called algorithms) and shared them with the world.

With the success of DeepMind's AlphaGo system defeating the world Go champion, reinforcement learning has achieved significant attention among researchers and developers. Deep reinforcement learning has become one of the most significant techniques in AI that is also being used by the researchers in order to attain artificial general intelligence. Below here is a list of 10 best free resources, in no particular order to learn deep reinforcement learning using TensorFlow. About: This tutorial "Introduction to RL and Deep Q Networks" is provided by the developers at TensorFlow. The topics include an introduction to deep reinforcement learning, the Cartpole Environment, introduction to DQN agent, Q-learning, Deep Q-Learning, DQN on Cartpole in TF-Agents and more.

We recommend these YouTube channels regardless of your machine learning experience, whether you have a computer science degree or just a passing interest in AI. You'll soon be on the way toward mastering the basics of AI, machine learning, and computer science in no time, through easy-to-follow demos and tutorial videos. The official Deep Learning AI YouTube channel has video tutorials from the deep learning specialization on Coursera. Artificial Intelligence -- All in One: This YouTube channel has tutorial videos related to science, technology, and artificial intelligence. Andrew Ng: Andrew Ng is a computer scientist and entrepreneur, co-founder of Google Brain, former VP & Chief Scientist at Baidu, adjunct professor at Stanford University.

If you are a student or a professional looking for various open-source computer vision projects, then, this article is here to help you. The computer vision projects listed below are categorized in an experience-wise manner. All of these projects can be implemented using Python. Face and Eyes Detection is a project that takes in a video image frame as an input and outputs the location of the eyes and face (in x-y coordinates) in that image frame. The script is fairly easy to understand and uses Haar Cascades for detecting the face and the eyes if found in the image frame.