The snakes behind the blockbuster are megafauna throwbacks. Breakthroughs, discoveries, and DIY tips sent every weekday. At roughly the length of a small school bus, anacondas are famously some of the world's largest snakes. Now fossil evidence proves that these enormous reptiles are also glimpses of an ancient world. According to a study published on December 1st in the, anacondas reached their maximum length around 12.4 million years ago--and have remained giants ever since.

Breakthroughs, discoveries, and DIY tips sent every weekday. South America's lush Amazon region is a biodiversity hotspot, which means that every living thing must find a way to co-exist. Even some of the most feared snakes on the planet–anacondas. In a paper published June 16 in the journal Frontiers in Amphibian and Reptile Science, conservation biologists Beatriz Cosendey and Juarez Carlos Brito Pezzuti from the Federal University of Pará's Center for Amazonian Studies in Brazil, analyze the key points behind the interactions between humans and the local anaconda populations. Ahead of the paper's publication, the team at Frontiers conducted this wide-ranging Q&A with Conesday.

What's up people, this is Cypherlynk and I have spent 10 hours trying to create a neural network and run it with my GPU, here are all the problems I faced while doing it. So as I told you before I know pytorch and I have been trying to switch from PyTorch to Tensorflow. So I was trying to create a Neural Network for learning just the basics of CNNs, all the math and theory behind it. But as soon as I finished the theory when I started to code it out, I did something really embarrassing, it was a beginner-level mistake but the moment I know what was wrong I was able to fix it. So when I was processing my data I imported 2 datasets from keras (The CIFAR-10 and FASHION MNIST).

Artificial intelligence (AI) is intelligence -- perceiving, synthesizing, and inferring information -- demonstrated by machines. Today, AI is no longer profound technology in a science lab. Instead, it is at amateurs' fingertips to create decent artwork, generate sophisticated conversation, and perform other intelligent tasks using DALL·E, Stable Diffusion, GPT-3, ChatGPT, Point·E, Whisper, etc. Have you ever wondered how a realistic image is generated by a natural language description? The intelligence comes from Machine Learning (ML), the study of computer algorithms that can improve automatically through experience and by the use of data. These textbook algorithms are publicly available and ready to be used.

Think AI is just coming for customer service jobs? Think again, say AI experts, who point to recent advances in large language models as evidence that white-collar and professional jobs will be disrupted too. Figuring out how AI and humans will coexist in the workplace is shaping up to be a key conversation for 2023 and beyond. "I think there are traditional white-collar businesses, white-collar professions that are going to be transformed by some of the innovation in large language models and AI technologies," said Peter Wang, the CEO of Anaconda, a provider of data science tools. "And that is going to create really interesting social and cultural dynamics that will basically settle out over the rest of this decade and reverberate into the 2030s."

"I felt there's a gap between what I learned in school, and what I actually do, and I also feel very insecure sometimes," she said. "I didn't know a lot of other data scientists who worked in the industry, so I wished I could have a community and talk to them." Essentially, said Liu, a data scientist takes something raw and translates it into something meaningful. The power of data science, she explained, is making sense of the past to make a recommendation for the future. "A data scientist is basically someone who solves a business problem with data," she explained.

We study the problem of non-stationary dueling bandits and provide the first adaptive dynamic regret algorithm for this problem. The only two existing attempts in this line of work fall short across multiple dimensions, including pessimistic measures of non-stationary complexity and non-adaptive parameter tuning that requires knowledge of the number of preference changes. We develop an elimination-based rescheduling algorithm to overcome these shortcomings and show a near-optimal $\tilde{O}(\sqrt{S^{\texttt{CW}} T})$ dynamic regret bound, where $S^{\texttt{CW}}$ is the number of times the Condorcet winner changes in $T$ rounds. This yields the first near-optimal dynamic regret algorithm for unknown $S^{\texttt{CW}}$. We further study other related notions of non-stationarity for which we also prove near-optimal dynamic regret guarantees under additional assumptions on the underlying preference model.

To further strengthen our commitment to providing industry-leading coverage of data technology, VentureBeat is excited to welcome Andrew Brust and Tony Baer as regular contributors. Data science is a quickly growing technology as organizations of all sizes embrace artificial intelligence (AI) and machine learning (ML), and along with that growth has come no shortage of concerns. The 2022 State of Data Science report, released today by data science platform vendor Anaconda, identifies key trends and concerns for data scientists and the organizations that employ them. Among the trends identified by Anaconda is the fact that the open-source Python programming language continues to dominate the data science landscape. Among the key concerns identified in the report was the barriers to adoption of data science overall.

This course offers an insightful introduction to the world of Machine Learning. Here, students learn the foundational concepts, starting with installing a Python Environment, using Anaconda. From there, students explore beginner-level classification models using Scikit-learn and Spicy with Anaconda. This course also offers a great introduction to working with Data Sets and formulating predictions using KNeighborsClassifier. "Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks."

Gamestonk Terminal is an awesome stock and crypto market terminal that has been developed for fun, while I saw my GME shares tanking. Gamestonk Terminal provides a modern Python-based integrated environment for investment research, that allows the average joe retail trader to leverage state-of-the-art Data Science and Machine Learning technologies. As a modern Python-based environment, GamestonkTerminal opens access to numerous Python data libraries in Data Science (Pandas, Numpy, Scipy, Jupyter), Machine Learning (Pytorch, Tensorflow, Sklearn, Flair), and Data Acquisition (Beautiful Soup, and numerous third-party APIs). Our current recommendation is to use this project with Anaconda's Python distribution – either full Anaconda3 Latest or Miniconda3 Latest. If you decided to add Machine Learning features at a later point, you will likely have better user experience with Anaconda's Python distribution.