A staff member displays a DJI Phantom 3 4K drone during CES (Consumer Electronics Show) in Las Vegas, Nevada. It may come as a surprising fact that there are now 14 Chinese AI companies valued at $1 billion or more. These unicorns are worth a combined $40.5 billion, according to a report China Money Network recently released during the World Economic Forum's Summer Davos gathering in Beijing. Just to put these numbers in perspective. Google bought DeepMind for over $500 million in 2014. Chinese voice recognition giant iFlytek Co. has a market capitalization of 63 billion yuan ($9.2 billion). Chinese AI startups raised $27.7 billion via 369 VC deals in 2017, according to a recent report from Tsinghua University. So naturally, it raises questions on if there is a bubble waiting to pop in the Chinese AI space. How could these companies, with an average age of less than five years, be worth so much money?

Nowadays, AI is all around us. Google exemplifies this by utilizing Machine Learning within Gmail to filter spam messages. Netflix and Amazon also utilize Deep Learning to predict your next movie or purchase. You might be hearing these terms appear more frequently and it would make sense due to the advent of rapid Big Data and analytics growth in the industrial and business workforce. You've hear these buzzwords consistently enough to peak your interest -- but what is the real meaning of Artificial Intelligence, Machine Learning, and Deep Learning? John McCarthy first coined the term'Artificial Intelligence' in 1956 but AI has turned out to be more famous today due to advance algorithms and enhancements in computing power and capacity.

Machine learning and computer vision methods have recently received a lot of attention, in particular when it comes to data analytics. The success of deep neural networks that can help cars drive autonomously and make smartphones recognize speech and translate text attests to the value of using machine learning methods to tackle complex real-world problems. A further prominent example is the success of Google's AlphaGo AI that defeated the world champion Lee Sedol in playing Go. This is remarkable in particular since Go has previously been considered as one of the most complex games due to the larger number of game states. As the amount of data collected by cameras and scientific instruments continues to rise, automated analysis methods will become ever more important in the future. Reappearing workflows, such as the segmentation of structures, will likely be either fully automated or supported by computers.

These days we would hardly find any enterprise which is not utilizing the power of Machine Learning (ML) or Artificial Intelligence (AI). Every business has now got activities driven by AI to revolve around. Day by day organizations are becoming dependent and automating tasks for efficient results and high accuracy. AI achieves this accuracy with the help of deep learning algorithms. For instance, you can have a look at the interactions we do with Alexa or Google search.

In this tutorial, you will learn how to use OpenCV to perform face recognition. To build our face recognition system, we'll first perform face detection, extract face embeddings from each face using deep learning, train a face recognition model on the embeddings, and then finally recognize faces in both images and video streams with OpenCV. Today's tutorial is also a special gift for my fiance e, Trisha (who is now officially my wife). Our wedding was over the weekend, and by the time you're reading this blog post, we'll be at the airport preparing to board our flight for the honeymoon. To celebrate the occasion, and show her how much her support of myself, the PyImageSearch blog, and the PyImageSearch community means to me, I decided to use OpenCV to perform face recognition on a dataset of our faces. You can swap in your own dataset of faces of course! All you need to do is follow my directory structure in insert your own face images. As a bonus, I've also included how to label "unknown" faces that cannot be classified with sufficient confidence. To learn how to perform OpenCV face recognition, just keep reading!

You can implement your own deep learning algorithms from scratch using Python or any other programming language. When you start implementing more complex models such as Convolutional Neural Network (CNN) or Recurring Neural Network (RNN) then you will realize that it is not practical to implement very large models from scratch. There are many deep learning frameworks available in the market that makes it easy for you to implement neural networks. Each of these frameworks has a dedicated user and developer community and you will find a lot of articles on the internet comparing the pros and cons of these frameworks. Each one of them has their own advantages and disadvantages but surely they are evolving and getting better month to month.

Much of our curriculum is based on feedback from corporate and government partners about the technologies they are using and learning. In addition to their feedback we wanted to develop a data-driven approach for determining what we should be teaching in our data science corporate training and our free fellowship for masters and PhDs looking to enter data science careers in industry. Below is a ranking of 23 open-source deep learning libraries that are useful for Data Science, based on Github and Stack Overflow activity, as well as Google search results. The table shows standardized scores, where a value of 1 means one standard deviation above average (average score of 0). For example, Caffe is one standard deviation above average in Github activity, while deeplearning4j is close to average.

Artificial Intelligence (AI) has the peculiar ability to simultaneously amaze, enthrall, leave us gasping and intimidate. The possibilities of AI are innumerable and they easily surpass our most artistically fecund imaginations. What all we read in science fiction novels or saw in movies like'The Matrix' could someday materialize into reality. Bill Gates, the founder of Microsoft, recently said that'AI can be our friend' and is good for the society. From decision-making to computing to robotics to vehicles and even cosmetics, AI has left its mark everywhere and it will usher in the grandest social engineering experiment in the history of the world.

All software programs released in the market have undergone the certification from testing department from the parent company. In short, the testing process is done to ensure every failure is rectified and then released to the market. Usually, every software program, to become successful, is dependent on the satisfaction of the users. So, in the testing process, every aspect of risks, problems and challenges are conducted in the software program to prevent any type of failure. In recent times, software is growing by leaps and bounds.

I remember the journey of creating my first deep learning network. It all started by typing that first search into Google to understand the technology: "What is deep learning?" After hours of deep learning trial and error, I was left with a painstaking discovery: this is going to take a lot of time. This is not another "what is deep learning?" I'm here to talk about the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage.