We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We propose two alternative divergence minimization approaches to learn the generative model and recognition model jointly. The first one treats all variables as a whole, while the second one utilizes the structural information by checking the individual local factors defined by the generative model and works better in practice. Finally, we present two important instances of Graphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN), which can successfully learn the discrete and temporal structures on visual datasets, respectively.

Machine learning is a subfield of computer science that gives computers the ability to learn without being explicitly programmed. Deep Learning is the next big thing and a part of machine learning. Its favorable results in applications with huge and complex data is remarkable. R is one of the most popular programming languages among the data science professionals. So, if you're a data science professional who wants to learn machine learning and deep learning with R, then go for this Learning Path.

A trio of students from the University of Glasgow have developed a sophisticated artificial intelligence which could underpin the next phase of gravitational wave astronomy. In a new paper published today in the journal Physical Review Letters, the researchers discuss how they used artificial intelligence tools to train an AI'brain' to search for gravitational wave signals. Gravitational waves, ripples in spacetime caused by massive astronomical events, were first hypothesised by Albert Einstein in 1915. It took another century before the Laser Interferometry Gravitational-Wave Observatory (LIGO) detectors in the United States first picked up the very faint signals from the collision of binary black holes. Since that historic first detection in September 2015, the Advanced LIGO and European VIRGO detectors have picked up numerous signals from other binary black holes and one from the collision of binary neutron stars.

The Chinese government's plans for mass surveillance using facial recognition have received a boost from one of the country's tech powerhouses, after Alibaba led a $600m investment in SenseTime, which develops technology for tracking individuals. The company is working on facial and object recognition technology that accurately can spot people using cameras, recently demonstrated on CCTV in Beijing. Honda is using SenseTime for its driverless car research and development and it is also being used at shopping counters that allows customers to check-out using their faces. SenseTime already smashed the record for AI funding, beating British competitor DeepMind which was bought by Google for an...

Hard to believe, but the R programming language has been with us since 1993. A quarter century has now passed since the authors Gentleman and Ihaka originally conceived the R platform as an implementation of the S programming language. Continuous global software development has taken the original concepts originally inspired by John Chambers' Scheme in 1975 to now include parallel computing, bioinformatics, social science and more recently complex AI and deep learning methods. Layers have been built on top of layers and today's R looks nothing like 1990's R. So where are we at, especially with the emerging opportunities for deep learning on the horizon?

An example of a Pokedex (thank you to Game Trader USA for the Pokedex template!) When I was a kid, I was a huge Pokemon nerd. I collected the trading cards, played the Game Boy games, and watched the TV show. If it involved Pokemon, I was probably interested in it. Pokemon made a lasting impression on me -- and looking back, Pokemon may have even inspired me to study computer vision.

Keras is a deep learning library written in Python for quick, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because of its lightweight and very easy to use nature, Keras has become popularity in a very short span of time. So, if you are a data scientist with experience in machine learning with some exposure to neural networks, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a quick look at your learning journey.

In today's world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. Machine learning gives you unimaginably powerful insights into data. Deep learning is the next step to machine learning with a more advanced implementation.

TensorFlow* is one of the leading deep learning and machine learning frameworks today. Earlier in 2017, Intel worked with Google to incorporate optimizations for Intel Xeon and Xeon Phi processor based platforms using Intel Math Kernel Libraries (Intel MKL). These optimizations resulted in orders of magnitude improvement in performance – up to 70x[1] higher performance for training and up to 85x[2] higher performance for inference. In this blog we provide a performance update for a number of deep learning models running on the Intel Xeon Scalable processor. The Intel Xeon Scalable processor provides up to 28 cores, which brings additional computing power to the table compared to the 22 cores of its predecessor.

A trio of students from the University of Glasgow have developed a sophisticated artificial intelligence which could underpin the next phase of gravitational wave astronomy. In a new paper published today in the journal Physical Review Letters, the researchers discuss how they used artificial intelligence tools to train an AI'brain' to search for gravitational wave signals. Gravitational waves, ripples in spacetime caused by massive astronomical events, were first hypothesised by Albert Einstein in 1915. It took another century before the Laser Interferometry Gravitational-Wave Observatory (LIGO) detectors in the United States first picked up the very faint signals from the collision of binary black holes. Since that historic first detection in September 2015, the Advanced LIGO and European VIRGO detectors have picked up numerous signals from other binary black holes and one from the collision of binary neutron stars.