Self-driving cars, have rapidly become one of the most transformative technologies to emerge. Fuelled by Deep Learning algorithms, they are continuously driving our society forward, and creating new opportunities in the mobility sector. Deep Learning jobs command some of the highest salaries in the development world. This is the first, and only course which makes practical use of Deep Learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. With over 28000 students, Rayan is a highly rated and experienced instructor who has followed a "learn by doing" style to create this amazing course.

It's easy to see the impressive rise in popularity for "machine learning" but most IT people and executives often have trouble identifying where their business might actually apply machine learning (ML) or Deep LEarning (DL) to business problems. Market leaders are using Artificial Intelligence for data analytics, predictions, targeted recommendations, and even HR. The question is, how can AI benefit your business? Well, answering this question is the main objective of this course: learn what Machine Learning is and how to use it in advantage of your business. That way, you can take advantage of this tremendous opportunity and become a successful ML entrepreneur.

In an interesting turn of events, IIT Kharagpur, this week announced that they were to launch a new course on artificial intelligence and machine learning, specially designed for working professionals and engineering students. The programme, which will be of six months duration, will commence from March 2019 and will be conducted from IIT-Kgp institute units in Kharagpur, Bengaluru and Kolkata and possibly in Hyderabad as well. PP Chakrabarti, director at IIT Kharagpur, told the media on Thursday, "A rigorous AI programme for professionals is the need of the hour. The programme has been designed by IIT Kharagpur faculty in consultation with industry experts." This course will comprise 16 one-credit modules and one capstone project.

Editor's note: This is a summary of a series of articles written on this subject from our friends at ActiveWizards. As such, each article in the series is intended as a 10 minute tutorial on a particular Apache Spark topic. Apache Spark is a powerful open-source processing engine built around speed, ease of use, and sophisticated analytics. It has originally been developed at UC Berkeley in 2009, while Databricks was founded later by the creators of Spark in 2013. The Spark engine runs in a variety of environments, from cloud services to Hadoop or Mesos clusters.

Below you'll see a rundown of Artificial Intelligence Resources to Learn, how to start in Artificial Intelligence in Easy steps: This course gives the basics of Artificial Intelligence (AI), and apply them. Object intelligent agents to resolve real world problems including, search, games, machine learning, logic, and constraint satisfaction problems.

I absolutely love this course. This is such a comprehensive course that was well worth the money I spent and a lot more. Will definitely be looking at more Mammoth Interactive courses when I finish this.

This course is about deep learning fundamentals and convolutional neural networks. Convolutional neural networks are one of the most successful deep learning approaches: self-driving cars rely heavily on this algorithm. First you will learn about densly connected neural networks and its problems. The next chapter are about convolutional neural networks: theory as well as implementation in Java with the deeplearning4j library. The last chapters are about recurrent neural networks and the applications!

The vanishing gradients problem is one example of unstable behavior that you may encounter when training a deep neural network. It describes the situation where a deep multilayer feed-forward network or a recurrent neural network is unable to propagate useful gradient information from the output end of the model back to the layers near the input end of the model. The result is the general inability of models with many layers to learn on a given dataset or to prematurely converge to a poor solution. Many fixes and workarounds have been proposed and investigated, such as alternate weight initialization schemes, unsupervised pre-training, layer-wise training, and variations on gradient descent. Perhaps the most common change is the use of the rectified linear activation function that has become the new default, instead of the hyperbolic tangent activation function that was the default through the late 1990s and 2000s. In this tutorial, you will discover how to diagnose a vanishing gradient problem when training a neural network model and how to fix it using an alternate activation function and weight initialization scheme.

Are you looking to build your AI skills, meet some of the brightest minds in deep learning and actually see how these technologies can be applied to business? Then be sure to register for Think 2019. For the first time ever, Think 2019 will be held in San Francisco. Join us February 12-15 to get a deeper dive into Watson technology and see how companies are putting AI to work. How Mercedes Answers Car Manual Queries with Watson When was the last time you read your car's user manual?

Imitation learning is a paradigm that learns from expert demonstration to perform a task. The most straightforward approach of imitation learning is behavioral cloning (Pomerleau, 1991), which learns from expert trajectories to predict the expert action at any state. Despite its simplicity, behavioral cloning ignores the accumulation of prediction error over time. Consequently, although the learned policy closely resembles the expert policy at a given point in time, their trajectories may diverge in the long term. To remedy the issue of error accumulation, inverse reinforcement learning(Russell, 1998; Ng and Russell, 2000; Abbeel and Ng, 2004; Ratliff et al., 2006; Ziebart et al., 2008; Ho and Ermon, 2016) jointly learns a reward function and the corresponding optimal policy, such that the expected cumulative