Registration Link - https://bit.ly/3Aios5K 14 Days. 10 Speakers. All-Inclusive Program. Career Tips. Free of Charge. Have you ever dreamt of becoming a data science rockstar and launching a career in Silicon Valley? We know the fastest pathway and can’t wait to share it with you. 💁 ⚡ Register to the first edition of our well-packed ML marathon right now. During the 14 days of comprehensive online webinars you will: 📌 find out insider tips from the leading experts about how to quickly start a successful data science career in Silicon Valley; 📌 level up your theoretical knowledge and learn breakthrough approaches to the creation of turnkey ML solutions without coding; 📌 boost your practical skills and master the ways to solve real-world challenges with ML; 📌 discover how to create TinyML models and embed them into the edge devices; 📌 get an overview of the current industry landscape, latest ML trends, and tools. 🎁 All participants will have a chance to take part in a special competition by Neuton.AI. Build a predictive model with a preassigned dataset and compare its accuracy with Neuton’s model. The creator of the most accurate model will be awarded with a free 3-month premium subscription to the Neuton.AI Platform. Duration: 1.5 hours daily Time: 7:00 PM IST - 8:30 PM IST (+5.30 GMT) Join our marathon today to skyrocket your data science career tomorrow! 🚀 Program: Block 1: Career Prospects 👨💻 9/27/2021 Machine Learning in a Nutshell by Soham Sharma Bringing Silicon Valley to Student by bridging gap between colleges and real-world by Gurumurthy Yeleswarapu, Siliconvalley4u 9/28/2021 How to take up data career. Your Ticket to the BIG Data Science World: Enter the Largest International Community of DS and business experts, AI Guild by Dr. Chris Armbruster Block 2: Actionable AutoML Tools 🛠️ 9/29/2021 Master Data Science without a Single Line of Code, Leveraging Neuton.AI [Live Demo Included] by Alex Miller & Danil Zherebtsov Block 3: Theory & Practice 💻 9/30/2021 The Fundamentals of Linear Regression (Theory) by Pallab Nath 10/1/2021 The Fundamentals of Linear Regression (Practice) by Pallab Nath 10/2/2021 Introduction to Support Vector Machines (Theory) by Dr. Promit Ray 10/3/2021 Introduction to Support Vector Machines (Practice) by Dr. Promit Ray 10/4/2021 The Art of Logistic Regression (Theory) by Namita Konnur 10/5/2021 The Art of Logistic Regression (Practice) by Namita Konnur 10/6/2021 KNN | Tips and Tricks (Theory) by Vivek Nair 10/7/2021 KNN | Tips and Tricks (Practice) by Vivek Nair 10/8/2021 In-Depth: Decision Tree + Random Forest (Theory) by Suram Saraswati Anugna 10/9/2021 In-Depth: Decision Tree + Random Forest (Practice) by Suram Saraswati Anugna Block 4: Industry Trends 💡 10/10/2021 TinyML: AI Intelligence for Edge Devices [Case Included] by Danil Zherebtsov
Deep learning is on the frontline in a new age of piracy, outwitting attacks with pre-emptive tech, explains Yarden Gross, CEO and co-founder of Orca AI. Almost a decade has passed since piracy raged off Somalia, and yet the danger posed by maritime hijackings is as present as ever. The global pandemic last year sparked a resurgence of attacks, with piracy incidents doubling across Asia, in a worrying uptick also seen in the Gulf of Mexico and West Africa. The fallout from coronavirus, including the loss of key security personnel, turned quarantined vessels into easy targets. This wave has since receded a little, with the International Maritime Bureau reporting a 44 per cent YoY dip in piracy and armed robbery incidents in 2021.
Rep. August Pfluger joins'Fox & Friends First' and calls out Biden's handling of border crisis The feds have turned to cutting-edge cameras developed by a virtual reality wunderkind to help them monitor the southern border -- by creating an invisible border wall. The high-tech watch poles known as Autonomous Surveillance Towers are powered by solar energy and use artificial intelligence to detect movement along a two-mile radius, sending the information in real-time to agents patrolling the area. And they're now being installed at different points along the nearly 2,000 miles of the US-Mexico border. "The ASTs are in remote locations that are difficult to reach," Border Patrol agent Joel Freeland recently told The Post. "They operate 24-hours a day and are environmentally friendly because they rely entirely on solar power." The ASTs were developed by Palmer Luckey, the 28-year-old founder and designer of Oculus VR and Oculus Rift.
Can you imagine walking through a store and being served by a robot? Something like this will happen in the electronics department of the Palacio de Hierro located in Polanco, Mexico City. A robot developed by Intel will be the department store's new advisor, it will help users choose computers and other electronic devices. The humanoid combines artificial intelligence with the internet of things and cloud services. The robot has the ability to answer common questions through its voice interaction, as well as profile what each user will need and move to the correct counter to show the customer the product.
Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Social media users branded a humorous wedding cake topper that highlighted a groom's love for video games as offensive, but the couple behind the requested figurines say that's not what they meant. Perla Blanco and Gerardo Martinez got married in late July after four years of dating. Their special day, which was celebrated in Monterrey, Mexico, with close family and friends, went viral on TikTok when their guest Leonardo Aceves shared a clip of their unique wedding cake.
While Apple may have released Siri before Google Assistant and Amazon Alexa, in many ways its voice-activated assistant is the least advanced of the three. A lot of that has to do with the amount of data and training digital assistants need to understand different languages, dialects and speech patterns. In an effort to improve its digital assistant, Apple recently launched a study to collect speech data and feedback with the help of an app called Siri Speech Study. "The Siri Speech Study app allows participants to send certain data to Apple for product improvement, as detailed in the informed consent form," the company says in a listing spotted by TechCrunch. The software is available in the US, Canada, Germany, France, Hong Kong, India, Ireland, Italy, Japan, Mexico, New Zealand and Taiwan.
Reinventing itself so as not to disappear, to that point of no return, the coronavirus pandemic (COVID-19) brought thousands of companies of all sizes and sectors in Mexico. In this scenario, O ne of the most disruptive technologies exploited to achieve digitalisation and economic growth was the Artificial Intelligence (AI). "The pandemic taught companies the importance of adopting technologies such as AI to digitize, improve the quality of care they provide to their customers, in their services and enhance the work generated by their collaborators, this while reducing costs and increasing their income ", says Israel Alejandro Cauich Viñas, founder and CEO of SoldAI . According to the Digital Transformation index, prepared by Dell Technology, it was announced that during 2020 Mexico experienced an important advance regarding digital transformation, since 52% of the companies surveyed in the Mexican Republic considered themselves part from the category "digital adopters", that is, companies that are already investing in innovation . Although Artificial Intelligence is not a new topic in the country, since it has been working on it since the 1950s, only in recent years has AI become the most powerful tool and the great ally of national companies so as not to be left behind in this digital revolution and, thus, to continue growing and being competent in the market. " Technological change in various industries today presents an unbeatable opportunity to position ourselves within countries at the forefront in the development and implementation of AI.
In recent years, Mexican startups have emerged considerably, so much so that many of them have become benchmarks not only in the region, but throughout the world. The reasons are various, from the enormous talent and potential that entrepreneurs have to exploit new digital technologies, to the geostrategic position that the country has. Another factor that has a favorable influence is that currently in Mexico there are various supports, coming from both the private and government sectors, that promote the emergence of innovative and technological service startups . And it is that for the national economy to continue growing, industries must have businesses that bet on innovation and that implement 4.0 technologies such as: Artificial Intelligence (AI), Big Data, Robotics, Blockchain, Machine Learning, Cloud Computing, among other. From emerging companies That focus on fintech, e-commerce and retail solutions, there are many Mexican Artificial Intelligence startups with a global profile .
We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method consists of two heads that share the same backbone network - a "representation learning" head and a "clustering" head. The "representation learning" head captures fine-grained patterns of objects at the instance level which serve as clues for the "clustering" head to extract coarse-grain information that separates objects into clusters. The whole model is trained in an end-to-end manner by minimizing the weighted sum of two sample-oriented contrastive losses applied to the outputs of the two heads. To ensure that the contrastive loss corresponding to the "clustering" head is optimal, we introduce a novel critic function called "log-of-dot-product". Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets, improving over the best baseline by about 5-7% in accuracy on CIFAR10/20, STL10, and ImageNet-Dogs. Further, the "two-stage" variant of our method also achieves better results than baselines on three challenging ImageNet subsets.
However, data labeling is often time-consuming and costly, as it involves human expertise. Thus, it is common for computer vision to pretrain DNNs vate improvements to DNN training approaches. A pioneer on some large labeled dataset, e. g. ImageNet (Russakovsky work of Zhang et al. (2017) showed that the capacity of et al., 2015), and then to fine-tune the model to a specific modern DNNs is sufficient to fit perfectly even randomly downstream task. The self-supervised learning paradigm labeled data. According to classic learning theory, such a provides a human labeling-free alternative to the supervised huge capacity should lead to catastrophic overfitting, however, pretraining: recently developed contrastive self-supervised recent works (Nakkiran et al., 2020) show that in methods show results, comparable to ImageNet pretraining practice increasing DNN capacity further improves generalization.