As technology goes more and more towards Artificial Intelligence branches such as Machine Learning and Deep Learning technologies, data and information are getting more and more endangered by Fake News and tampered materials. Deep Fakes seems to be the most "promising" and dangerous example of this kind. Shortly, it allows creating a tampered video content by replace its behavior. Say, like replacing an actor's face with someone's else, as it happened to Gal Gadot (read more). Turns out there are out thousands of "faked" videos out in the network.

This is the first part of a three-part blog series. You can read the second part here and the third part here. The Internet of Things provides us with lots of sensor data. However, the data by itself does not provide value unless we can turn it into actionable, contextualized information. Big data and data visualization techniques allow us to gain new insights through batch-processing and off-line analysis.

As a result, in the early pe-riod, people use deep learning as a tool to learn high level features from low level cues usually hand-crafted. Feb 2017 - IEEE Transactions on Image Processing M. Well then write a Python script that will use OpenCV and GoogleLeNet pre-trained on ImageNet to classify images. This course will introduce the fundamental technologies for autonomous vehicle sensors, perception and machine learning, from electromagnetic spectrum characteristics and signal acquisition, vehicle extrospective sensor data analysis, perspective geometry models, image and point cloud processing, to machinedeep learning approaches. Using Keras and Deep Deterministic Policy Gradient to play TORCS. Deep learning is an exciting, young field that specializes in discovering and extracting intricate structures in large, unstructured datasets for parameterizing artificial neural networks with many layers.

Neural networks have been used for a wide variety of tasks across different fields. We'd like to do everything we could with a regular neural network, but we want to explicitly treat the inputs as images. We'll discuss a special kind of neural network called a Convolutional Neural Network (CNN) that lies at the intersection between Computer Vision and Neural Networks. CNNs are used for a wide range of image-related tasks such as image classification, object detection/localization, image generation, visual question answering, and more! We'll discuss the different kinds of layers in a CNN and how they function. Finally, we will build a historical CNN architecture called LeNet-5 and use it to recognize handwritten digits using the Keras library (on top of Tensorflow).

Neural nets have some challenges... but this design might be a solution for many of those. Neural nets are the core machinery that make deep learning so powerful. This radical new design scraps the layers entirely to overcome a major shortcoming in AI.

IE Business School is pioneering the usage of technology in finance within the Fintech focus. A fully fledged Python programming core course became mandatory in the Master in Finance in 2018 in order to leverage on technology applications such as machine learning and deep learning. Python is an open source, interpreted programming language, with a large set of advantages of which we can highlight flexibility, simplicity (upon developing new codes), a focus on functionality, with a range of libraries and with a very rapid and simple learning curve when it is compared to other languages. This is because of its simple and visual syntax as well as having a huge community of users who share codes in online platforms such as Github (where we can find libraries such as Keras, NumPy, Panda, Theano etc.). Its application is large and widely used in data analysis, big data, machine learning, web programming or finance processes. Because of all this, Python's popularity is growing both among individual users and companies.

LONDON – Following the launch of its AI inference chip last year, Habana Labs (Tel-Aviv, Israel) has unveiled an AI training chip built on the same architecture that can outpace the incumbent technology by a substantial margin, and features on-chip RoCE (remote direct memory access over Converged Ethernet) communications for scalability. While the company's inference chip, Goya, set records for ResNet-50 inference back in September 2018, the new training chip, Gaudi, offers similar high performance. Gaudi can process 1650 images per second at a batch size of 64 when training a ResNet-50 network, which Habana claims is a new world record for this benchmark. This throughput is delivered at 140W power consumption, also a substantial advantage versus competing solutions, according to the company. Impressive, but is Habana's architecture designed specifically to beat the ResNet-50 benchmark, or will it offer similar throughput advantages for other types of neural networks?

When Ian Goodfellow dreamt up the idea of Generative Adversarial Networks (GANs) over a mug of beer back in 2014, he probably didn't expect to see the field advance so fast: In case you don't see where I'm going here, the images you just saw were utterly, undeniably, 100% … fake. Also, I don't mean these were photoshopped, CGI-ed, or (fill in the blanks with whatever Nvidia's calling their fancy new tech at the moment). I mean that these images are entirely generated through addition, multiplication, and splurging ludicrous amounts of cash on GPU computation. The algorithm that makes is stuff work is called a generative adversarial network (which is the long way of writing GAN, for those of you still stuck in machine learning acronym land), and over the last few years, there have been more innovations dedicated to making it work than there have been privacy scandals at Facebook. Summarizing every single improvement to the 2014 vanilla GANs is about as hard as watching season 8 of Game of Thrones on repeat. I'm not going to explain concepts like transposed convolutions and Wasserstein distance in detail. Instead, I'll provide links to some of the best resources you can use to quickly learn about these concepts so that you can see how they fit into the big picture. If you're still reading, I'm going to assume that you know the basics of deep learning and that you know how convolutional neural networks work.

New research from the University of Kansas shows machine learning is capable of identifying insects that spread the incurable disease called Chagas with high precision, based on ordinary digital photos. The idea is to give public health officials where Chagas is prevalent a new tool to stem the spread of the disease and eventually to offer identification services directly to the general public. Chagas is particularly nasty because most people who have it don't know they've been infected. But according to the Centers for Disease Control and Prevention, some 20 percent to 30 percent of the 8 million people with Chagas worldwide are struck at some later point with heart rhythm abnormalities that can bring on sudden death; dilated hearts that don't pump blood efficiently; or a dilated esophagus or colon. The disease is caused most often when triatomine bugs -- more commonly known as "kissing bugs" -- bite people and transmit the parasite Trypanosoma cruzi into their bloodstreams.

Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. FaceNet is a face recognition system developed in 2015 by researchers at Google that achieved then state-of-the-art results on a range of face recognition benchmark datasets. The FaceNet system can be used broadly thanks to multiple third-party open source implementations of the model and the availability of pre-trained models. The FaceNet system can be used to extract high-quality features from faces, called face embeddings, that can then be used to train a face identification system. In this tutorial, you will discover how to develop a face detection system using FaceNet and an SVM classifier to identify people from photographs. How to Develop a Face Recognition System Using FaceNet in Keras and an SVM Classifier Photo by Peter Valverde, some rights reserved. Face recognition is the general task of identifying and verifying people from photographs of their face.