Learn to detect obstacles in lidar point clouds through clustering and segmentation, apply thresholds and filters to radar data in order to accurately track objects, and augment your perception by projecting camera images into three dimensions and fusing these projections with other sensor data. Combine this sensor data with Kalman filters to perceive the world around a vehicle and track objects over time. Learn to fuse data from three of the primary sensors that robots use: lidar, camera, and radar.

In this program, students will enhance their skills by building and deploying sophisticated machine learning solutions using popular open source tools and frameworks, and gain practical experience running complex machine learning tasks using the built-in Azure labs accessible inside the Udacity classroom. In this program, students will enhance their skills by building and deploying sophisticated Machine Learning (ML) solutions using popular open source tools and frameworks and gain practical experience by using the built-in Azure labs accessible inside the Udacity classroom to run complex machine learning tasks for no additional cost.

In this program, you'll sharpen your Python skills, apply C, apply matrices and calculus in code, and touch on computer vision and machine learning. These concepts will be applied to solving self-driving car problems. At the end, you'll be ready for our Self-Driving Car Engineer Nanodegree program!

What is the difference between the Flying Car Nanodegree program and the Self-Driving Car Engineer Nanodegree program or the Robotics Software Engineer Nanodegree program? The Flying Car Nanodegree program is a specialized program for aerial vehicles. The focus will be on developing the skills to build an autonomous aircraft system, with a focus on quadrotors. This means a unique emphasis on planning and autonomy for three-dimensional mobility, involving hands-on projects in simulation, with the opportunity to port your code to real drones. The Robotics Software Engineer Nanodegree program provides an introduction to software and artificial intelligence as applied to robotics.

In this one-hour webinar followed by live Q&A, you will learn about the STM32Cube function packs that allow you to run AI computer vision applications on STM32 microcontrollers, such as the FP-AI-VISION1. We will showcase several examples of computer vision applications based on Convolutional Neural Networks, including people-counting and facial recognition demos, and present the roadmap for edge AI computer vision on STM32 MCUs. After the webinar, there will be a 15-minute Q&A session where our experts will be available to answer any questions. This webinar will be broadcast twice, at convenient times for international audiences. Please review the times listed below and register for the most appropriate option for your time zone.

Welcome to this tutorial on building a deep learning model for malware classification. As we all know, malware detection is one of the most challenging task for IT companies. A huge effort is made by antivirus vendors to always update their solution in order to thwart new types of computer viruses. On the other hand, malware signature is constantly modified in order to bypass antivirus. The good news here is that great deal of new malware versions are slight modifications of older ones . In other words, the binary of new malware versions differs very slightly from those of versions that are already managed by antiviruses.

Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are more important than using a simple and understandable model. As such, they are often used by top and winning participants in machine learning competitions like the One Million Dollar Netflix Prize and Kaggle Competitions. Modern machine learning libraries like scikit-learn Python provide a suite of advanced ensemble learning methods that are easy to configure and use correctly without data leakage, a common concern when using ensemble algorithms. In this crash course, you will discover how you can get started and confidently bring ensemble learning algorithms to your predictive modeling project with Python in seven days. This is a big and important post.

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later. The field of AI was formally founded in 1956. But it's only now--more than six decades later--that AI is expected to revolutionize the way humanity will live and work in the coming decades.

Deep Learning: Advanced NLP and RNNs Over the past year, many of you have been asking for a followup on my RNN and Deep NLP courses. I am glad to announce that today, that course is here. Highest Rated What you'll learn It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then?

Description NumPy is a leading scientific computing library in Python. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy is one of the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy to the advanced NumPy. This course will give you a solid understanding of NumPy and its functions. At the end of the course, you should be able to write complex arrays for real-life projects.