We develop a new theoretical framework to analyze the generalization error of deep learning, and derive a new fast learning rate for two representative algorithms: empirical risk minimization and Bayesian deep learning. The series of theoretical analyses of deep learning has revealed its high expressive power and universal approximation capability. Although these analyses are highly nonparametric, existing generalization error analyses have been developed mainly in a fixed dimensional parametric model. To compensate this gap, we develop an infinite dimensional model that is based on an integral form as performed in the analysis of the universal approximation capability. This allows us to define a reproducing kernel Hilbert space corresponding to each layer. Our point of view is to deal with the ordinary finite dimensional deep neural network as a finite approximation of the infinite dimensional one. The approximation error is evaluated by the degree of freedom of the reproducing kernel Hilbert space in each layer. To estimate a good finite dimensional model, we consider both of empirical risk minimization and Bayesian deep learning. We derive its generalization error bound and it is shown that there appears bias-variance trade-off in terms of the number of parameters of the finite dimensional approximation. We show that the optimal width of the internal layers can be determined through the degree of freedom and the convergence rate can be faster than $O(1/\sqrt{n})$ rate which has been shown in the existing studies.

Video sequences contain rich dynamic patterns, such as dynamic texture patterns that exhibit stationarity in the temporal domain, and action patterns that are non-stationary in either spatial or temporal domain. We show that a spatial-temporal generative ConvNet can be used to model and synthesize dynamic patterns. The model defines a probability distribution on the video sequence, and the log probability is defined by a spatial-temporal ConvNet that consists of multiple layers of spatial-temporal filters to capture spatial-temporal patterns of different scales. The model can be learned from the training video sequences by an "analysis by synthesis" learning algorithm that iterates the following two steps. Step 1 synthesizes video sequences from the currently learned model. Step 2 then updates the model parameters based on the difference between the synthesized video sequences and the observed training sequences. We show that the learning algorithm can synthesize realistic dynamic patterns.

Twitter CEO Jack Dorsey discussed how the social network is using machine learning and deep learning to populate its timeline during the company's annual shareholders' meeting conference call Monday. Dorsey said showing Twitter users what's happening and delivering them the news of the day is the social network's "singular job," and he discussed changes to the timeline over the past year-and-a-half: This is where people spend almost all of their time on Twitter. This is where they get all of their news, see everything that's going on, see what people think about what's going on. And for the longest time, it just hasn't been personalized. It's been completely ordered by recency.

The term'Neural' is derived from the human (animal) nervous system's basic functional unit'neuron' or nerve cells which are present in the brain and other parts of the human (animal) body. Dendrite - It receives signals from other neurons. Soma (cell body) - It sums all the incoming signals to generate input. Axon - When the sum reaches a threshold value, neuron fires and the signal travels down the axon to the other neurons. The amount of signal transmitted depend upon the strength (synaptic weights) of the connections.

If you want a thorough, everyday clean, maybe you should consider using robots. The Roomba 650 Vacuuming Robot provides a thorough clean at the push of a button.

Financial services have been revolutionised over the last 20 years by increasingly powerful technology such as big data analytics, neural networks, evolutionary algorithms, and machine learning. Now, Fintech is on the cusp of a truly revolutionary moment, the integration of AI and deep learning into financial services. This combination really has the potential to revolutionise money and the global financial landscape in ways we never could have imagined 20 years ago. This is hardly by accident, a 2015 report by Accenture tracked the global investment in Fintech; revealing it has jumped from $930 million in 2008 to over $12 billion by the start of 2015. The explosion of data over the last 10 years has been incredible, and has been so vast that there has been little way to comprehend it without intelligent automated support.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Data science, deep learning, citizen scientists, data lake, big data, AI, machine learning, hadoop, Spark, deep learning....Yes, I get these and breathe them every single day. But let me ask you...Have you been asked any of following questions.... As a data scientist, how do you bridge a gap between algorithms and business needs? If data science can predict, prescribe, and optimize, why few sectors like retailers are not able to react with the right offer at the right time (although they claim to do so? Why there is a culture battle?

Chinese internet giant Baidu says it plans to leverage advanced cloud-computing to analyse the online data of millions of its users to help companies improve their marketing campaigns. The Chinese search engine giant, which has real-time search data on more than 700 million internet users, is able to analyse individual users through its cloud arm's artificial intelligence (AI), big data and cloud computing technologies, Yin Shiming, vice president and general manager of Baidu Cloud Computing, said in Shenzhen. "AI is bringing in new ways of thinking for many traditional industries," said Yin, who cited the recent battle between Google DeepMind's AlphaGo computer program and Chinese Go master Ke Jie as supporting his view that the development of AI technology has stepped up. "Our Marketing Cloud, backed by Baidu Cloud's data and technology, is not just saving resources and costs, but making marketing easier," Yin said. Despite challenges from other local search brands such as Sogou and Qihoo 360, Baidu's dominance in online search has hardly swayed over the years, accounting for about 75 per cent of the search market.