"Honestly Mike your classes speak for themselves. They're informative, concise and just really well put together. They're exactly the kind of courses I look for." This is the second course in a series designed to prepare you for becoming a machine learning engineer. I'll keep this updated and list only the courses that are live.

This course is for people who want to learn how to do things, not just to fill their heads with important concepts, paradigms, and heaps of information they kind of know but have no idea how to use. Apache Hive is an easy SQL based tool that allows to process large amounts of data on Hadoop fast. Hive gained popularity immediately after Hadoop MapReduce became widely used as it allows to work with data by means of SQL queries. It is used by many organisations to process their data. This course shows a number of interesting Hive queries and explains what Hive UDFs are.

This is the fourth course in the series designed to prepare you for a real world job in the machine learning space. I'd highly recommend you take the courses serially. People love building models and many think that machine learning engineers sit around and build models all day. Take the courses in order to understand what machine learning engineers really do. In this course we are going to learn SciKit-Learn using a lab integrated approach.

A basic grounding in the principles and practices around artificial intelligence (AI), automation and cognitive systems is something which is likely to become increasingly valuable, regardless of your field of business, expertise or profession. Fortunately, today you don't have to take years out of your life studying at university to become familiar with this seemingly hugely complex technology. A growing number of online courses have sprung up in recent years covering everything from the basics to advanced implementation. Some are aimed at people who want to dive straight into coding their own artificial neural networks, and understandably assume a certain level of technical ability. Others are useful for those who want to learn how this technology can be applied by anyone, regardless of prior technical expertise, to solving real-word problems.

The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will use Deep Learning with Convolutional Neural Networks, and use TensorFlow to build neural networks.

Get your team access to Udemy's top 2,500 courses anytime, anywhere. Deep Learning is the most exciting, highly sought and one of the fastest-growing field nowadays. If you want to pursue a career in Artificial Intelligence, Deep Learning will help you do so. Actually Deep Learning is a subfield of Machine learning concerned with algorithms inspired by artificial neural networks i.e the structure and function of the brain. DL is a key enabler of AI powered technologies being developed across the globe.

There can be little doubt that the single hottest career in the data field is the data scientist or BI developer skilled in predictive analytics. Yes, Big Data is on everyone's lips but what happens after that big data is ingested into a data lake? The answer is predictive analytics. Because we live in the big data era, machine learning has become much more popular in the last few years. Having lots of data to work with in many different areas lets the techniques of machine learning be applied to a broader set of problems.

My course does exactly what the title describes in a simple, relatable way. I help you to grasp the complete start to end concepts of fundamental deep learning. On your own it can be quite confusing, difficult and frustrating. I've been through the process myself, and with the help of lifelong ... I want to share this with my fellow beginners, developers, AI aspirers, with you. I will give you straightforward examples, instructions, advice, insights and resources for you to take simple steps to create your own neural networks from scratch.

There is an increasing interest on accelerating neural networks for real-time applications. We study the student-teacher strategy, in which a small and fast student network is trained with the auxiliary information learned from a large and accurate teacher network. We propose to use conditional adversarial networks to learn the loss function to transfer knowledge from teacher to student. The proposed method is particularly effective for relatively small student networks. Moreover, experimental results show the effect of network size when the modern networks are used as student. We empirically study the trade-off between inference time and classification accuracy, and provide suggestions on choosing a proper student network.

This new Chair, funded by Google France, aims to support the training of a new generation of talents in artificial intelligence in terms of training, research, and international outreach. The new Artificial Intelligence and Advanced Visual Computing Master's program of École Polytechnique, offered in partnership with Inria, ENSTA ParisTech, and Télécom ParisTech, will benefit from support as it welcomes its first class in September 2018. Google France will offer students opportunities in its research internship programs. AI awareness seminars in ethics and law, as well as roundtables and workshops, will also be conducted to allow students to succeed in the professional world and in scientific research in the field of AI. An invitation program for world-renowned professors will also be funded by the Chair to support these research activities and to enrich the academic offer of the Artificial Intelligence and the Advanced Visual Computing Master's program.