Overview of Udacity Artificial Intelligence Engineer Nanodegree, Term 1


After finishing Udacity Deep Learning Foundation I felt that I got a good introduction to Deep Learning, but to understand things, I must dig deeper. Besides I had a guaranteed admission to Self-Driving Car Engineer, Artificial Intelligence, or Robotics Nanodegree programs.

Deep Learning A-Z : Hands-On Artificial Neural Networks


Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

Deep Learning in Computer Vision Coursera


About this course: Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation.

If launching a career in AI is your thing, this online course can get you started


Just to let you know, if you buy something featured here, Mashable might earn an affiliate commission. From self-driving cars and a cucumber sorter to disaster-prediction programs and cancer-detection systems, current applications of artificial intelligence technology would have The Jetsons blushing and Asimov deeply shook. According to one survey of industry experts at an AI conference, intelligent machines will be able to perform any intellectual task a human can perform by the year 2050. As such, there's a growing need among companies for AI professionals that know the ins and outs of machine learning (ML) -- giving a device access to data and letting it learn for itself -- as well as its newer subset, deep learning. Capable of making independent decisions about unstructured data, deep learning networks have been described by Forbes as being capable of unlocking "the treasure trove of unstructured big data for those with the imagination to use (them)."

Machine Learning with scikit-learn and Tensorflow


Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks. We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification.