This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. Also, you don't have to be attend any ML course before.

It's become a custom for some protesters to cover their faces during public demonstrations. Now, it seems, technology could outwit them: a team of engineers has created an algorithm that can identify faces that are partially covered. The algorithm identifies faces using angles at 14 different points on the face, according to a paper published on the preprint server arXiv to be presented at the IEEE International Conference on Computer Vision Workshops in October. The researchers trained and validated the algorithm, which relies on a form of artificial intelligence called deep learning, using a dataset of 1500 images of 25 human faces. Each face was partially obscured by one or more of ten disguises (such as sunglasses, a face scarf, or a hat) and eight complex backgrounds to simulate real-world photos.

Researchers from McGill University in Canada reveal how they used machine-learning techniques and beta-amyloid imaging to predict Alzheimer's development in patients with mild cognitive impairment (MCI) up to 2 years before symptoms arose. Co-lead study author Dr. Pedro Rosa-Neto, of the departments of Neurology & Neurosurgery and Psychiatry at McGill University, and colleagues recently reported their findings in the journal Neurobiology of Aging. MCI is a condition characterized by a decline in cognitive functions - such as memory and thinking skills - that is noticeable, but which does not impact a person's ability to carry out everyday tasks. According to the Alzheimer's Association, studies have suggested that around 15 to 20 percent of adults aged 65 and older are likely to have MCI, and these individuals are at greater risk of Alzheimer's than the general population. At present, there is no way to predict which MCI patients will go on to develop Alzheimer's disease, but Dr. Rosa-Neto and colleagues believe that their algorithm has the potential to fulfill this need.

Cloudera said today as part of its second-quarter earnings report that it is acquiring Fast Forward Labs, a startup that gives companies the latest information on how to apply machine learning and AI to their businesses, as well as consulting. The company's stock has weathered somewhat of a beating since it went public, though it was able to beat Wall Street's expectations today. But the more interesting news is the acquisition of Fast Forward Labs, a company that specializes in consulting with larger enterprises about emerging trends in machine learning that can help their businesses grow. Cloudera specializes in operating on top of open-source technology, looking to deliver an enterprise-grade product for larger organizations. "On the way, we built a profitable company with real impact on our clients' products and businesses. I'm proud of what we've accomplished," CEO and co-founder Hilary Mason said in a post announcing the acquisition.

Apart from performing for our clients, InData Labs data science team is keen on taking part in top notch data science competitions, for example, Kaggle Competition. The team has recently shown one of the best results in Quora Question Pairs Challenge on Kaggle. The challenge is remarkable for a number of interesting findings and controversies among the participants, so let's dig deeper into the details of the competition and create a winning formula for data science and machine learning Kaggle competition. Quora is a Q&A site where anyone can ask questions and get answers. Quora audience is quite diverse. People use it for studying, work consultations and whenever they have second thoughts about almost anything.

Artificial intelligence can accurately guess whether people are gay or straight based on photos of their faces, according to new research that suggests machines can have significantly better "gaydar" than humans. The study from Stanford University – which found that a computer algorithm could correctly distinguish between gay and straight men 81% of the time, and 74% for women – has raised questions about the biological origins of sexual orientation, the ethics of facial-detection technology, and the potential for this kind of software to violate people's privacy or be abused for anti-LGBT purposes. The research found that gay men and women tended to have "gender-atypical" features, expressions and "grooming styles", essentially meaning gay men appeared more feminine and vice versa. The data also identified certain trends, including that gay men had narrower jaws, longer noses and larger foreheads than straight men, and that gay women had larger jaws and smaller foreheads compared to straight women.

People get up to weird things in New Zealand. At the University of Auckland, if you want to run hours upon hours of experiments on a baby trapped in a high chair, that's cool. You can even have a conversation with her surprisingly chatty disembodied head. BabyX, the virtual creation of Mark Sagar and his researchers, looks impossibly real. The child, a 3D digital rendering based on images of Sagar's daughter at 18 months, has rosy cheeks, warm eyes, a full head of blond hair, and a soft, sweet voice. When I visited the computer scientist's lab last year, BabyX was stuck inside a computer but could still see me sitting in front of the screen with her "father." To get her attention, we'd call out, "Hi, baby. Look at me, baby," and wave our hands. When her gaze locked onto our faces, we'd hold up a book filled with words (such as "apple" or "ball") and pictures (sheep, clocks), then ask BabyX to read the words and identify the objects.

We propose convolutional deep averaging networks (CDANs) for classifying and learning feature representations of datasets containing instances with unordered features, where each feature is considered a tuple composed of one or more values. CDANs accept variable-size input and are invariant to permutations of the input's order. In addition, as a side-effect of the training process, CDANs learn discriminative, nonlinear embeddings of individual input elements into a space of chosen dimensionality. Contrary to their name, which is inspired by the work of Iyyer et al. [11], CDANs could perhaps be more accurately termed convolutional deep pooling networks as we also consider the effects of functions other than averaging such as taking element-wise maximums or sums. A. Contributions We propose CDANs for classifying unordered feature sets. We show that a CDAN with nonlinear embeddings is competitive with and perhaps even superior to recurrent neural networks (RNNs) and known permutation-invariant architectures for classifying instances containing variablesize sets of unordered features. We also find that the type of pooling plays a significant role in determining the efficacy of the network with sum-pooling clearly outperforming maxand average-pooling.

The number of component classifiers chosen for an ensemble has a great impact on its prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size considering Majority Voting (MV) and Weighted Majority Voting (WMV). Almost all of them are designed for batch-mode, barely addressing online environments. The big data dimensions and resource limitations in terms of time and memory make the determination of the ensemble size crucial, especially for online environments. Our framework proves, for the MV aggregation rule, that the more strong components we can add to the ensemble the more accurate predictions we can achieve. On the other hand, for the WMV aggregation rule, we prove the existence of an ideal number of components equal to the number of class labels, with the premise that components are completely independent of each other and strong enough. While giving the exact definition for a strong and independent classifier in the context of an ensemble is a challenging task, our proposed geometric framework provides a theoretical explanation of diversity and its impact on the accuracy of predictions. We conduct an experimental evaluation with two different scenarios to show the practical value of our theorems.

In this paper, we present a simple analysis of {\bf fast rates} with {\it high probability} of {\bf empirical minimization} for {\it stochastic composite optimization} over a finite-dimensional bounded convex set with exponential concave loss functions and an arbitrary convex regularization. To the best of our knowledge, this result is the first of its kind. As a byproduct, we can directly obtain the fast rate with {\it high probability} for exponential concave empirical risk minimization with and without any convex regularization, which not only extends existing results of empirical risk minimization but also provides a unified framework for analyzing exponential concave empirical risk minimization with and without {\it any} convex regularization. Our proof is very simple only exploiting the covering number of a finite-dimensional bounded set and a concentration inequality of random vectors.