What can be a better way to spend your days listening to interesting bits about data science? My commute to work everyday is roughly one hour ( /- 15 minutes depending on the day). It's safe to say I cruise through A LOT of podcasts. The subjects I listen to range from True Crime, NFL Fantasy Football, Major League Baseball, and Data Science. This is my personal ranking/list of the best data science podcasts on SoundCloud, Apple Podcast, and Spotify.

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Scikit-Learn , Machine Learning, Tensorflow , and more!

These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

In recent years, there has been a surge in building and adopting machine learning (ML) tools. The use of GPUs to accelerate increasingly compute-intensive models has been a prominent trend. To increase user access, the Accelerated WEKA project provides an accessible entry point for using GPUs in well-known WEKA algorithms by integrating open-source RAPIDS libraries. In this post, you will be introduced to Accelerated WEKA and learn how to leverage GPU-accelerated algorithms with a graphical user interface (GUI) using WEKA software. This Java open-source alternative is suitable for beginners looking for a variety of ML algorithms from different environments or packages.

PyTorch is an open-source deep-learning library managed by Meta's AI team. PyTorch is purposely built for Python and is an easy way to start if you're new to developing AI projects. This article will give you a good introduction to how it works and provide you with a simple example of how to build a neural network to get you going. Finally, I provide you with some resources if you want to learn more about PyTorch and how to develop your own AI projects. A neural network is a computer system that is inspired by the way the brain works. Neural networks are composed of a series of algorithms that can learn to recognize patterns of input data.

Keras is an easy to use and powerful Python library for deep learning. There are a lot of decisions to make when designing and configuring your deep learning models. Most of these decisions must be resolved empirically through trial and error and evaluating them on real data. As such, it is critically important to have a robust way to evaluate the performance of your neural networks and deep learning models. In this post you will discover a few ways that you can use to evaluate model performance using Keras.

In 2022, the Engineering School of Information Technologies, Telecommunications and Control Systems of UrFU launched a new master's program, Artificial Intelligence Engineering. Here students will learn to create large software systems that use machine learning. Bachelor's graduates can enter the program, the main thing is to have a good basic knowledge of mathematics. Programming skills are welcome but not required. Everything that is necessary for work is taught in the master's program, including the Python programming language.

Artificial intelligence is growing at record-breaking speed, literally. Thanks to exponential development, AI has made its way to the Guinness book of world records. Below is a list of records in the AI domain. What started as a simple Bot Camp program became a world record for the largest artificial intelligence programming lesson. Capital One Services LLC hosted this camp as part of its Future Edge DFW initiative in Dallas, Texas, USA, on April 17 2019.

The Workshop Program of the Association for the Advancement of Artificial Intelligence's Thirty-Sixth Conference on Artificial Intelligence was held virtually from February 22 – March 1, 2022. There were thirty-nine workshops in the program: Adversarial Machine Learning and Beyond, AI for Agriculture and Food Systems, AI for Behavior Change, AI for Decision Optimization, AI for Transportation, AI in Financial Services: Adaptiveness, Resilience & Governance, AI to Accelerate Science and Engineering, AI-Based Design and Manufacturing, Artificial Intelligence for Cyber Security, Artificial Intelligence for Education, Artificial Intelligence Safety, Artificial Intelligence with Biased or Scarce Data, Combining Learning and Reasoning: Programming Languages, Formalisms, and Representations, Deep Learning on Graphs: Methods and Applications, DE-FACTIFY: Multi-Modal Fake News and Hate-Speech Detection, Dialog System Technology Challenge, Engineering Dependable and Secure Machine Learning Systems, Explainable Agency in Artificial Intelligence, Graphs and More Complex Structures for Learning and Reasoning, Health Intelligence, Human-Centric Self-Supervised Learning, Information-Theoretic Methods for Casual Inference and Discovery, Information Theory for Deep Learning, Interactive Machine Learning, Knowledge Discovery from Unstructured Data in Financial Services, Learning Network Architecture during Training, Machine Learning for Operations Research, Optimal Transports and Structured Data Modeling, Practical Deep Learning in the Wild, Privacy-Preserving Artificial Intelligence, Reinforcement Learning for Education: Opportunities and Challenges, Reinforcement Learning in Games, Robust Artificial Intelligence System Assurance, Scientific Document Understanding, Self-Supervised Learning for Audio and Speech Processing, Trustable, Verifiable and Auditable Federated Learning, Trustworthy AI for Healthcare, Trustworthy Autonomous Systems Engineering, and Video Transcript Understanding. This report contains summaries of the workshops, which were submitted by most, but not all the workshop chairs.

Abstract-- This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. Current technologies allow DeltaZ to be 3D-printed from soft and rigid materials so that it is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for a research setting. We also describe how the robot can be used as a reinforcement learning benchmark. Open-source 3D-printable designs and code are available to the public.