Data Science has become the new desirable IT job. While there are only few in the market conversant with the terms like python, machine learning, deep learning and transflow, it is also a fact that these skills are high in demand. Acadgild will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more. This 24 weeks long Data Science course has several advantages like 400 total coding hours and experienced industry mentors.

Artificial intelligence is winning the rat race. Google-owned DeepMind has built an artificial intelligence that is better at navigating a maze than humans. After it was trained with data on how rodents search for food, it mimicked the processes that allow mammals to get between destinations in the most efficient way. Humans and other mammals have neurons called "grid cells" that help us find our way as we navigate our surroundings.

DeepMind researchers have developed a neural network loosely modeled on mammalian brains to craft an artificially intelligent program capable of navigating through mazes. The results were published in a paper in the journal Nature on Wednesday. The grid-cell neural network is made up of three layers: a recurrent layer, a linear layer, and and an output layer. It's trained by following the paths of simulated rats shuffling about in a small 2D enclosure. The virtual rats trace the shape of the square-shaped or circular enclosure without ever touching the walls.

Advanced computing enables us to make connections like never before. We can teach machines to detect cancer, identify cybersecurity threats before they happen, and optimize business operations – the possibilities are endless. So how can machines continue to learn, without the dedication of abundant resources? Deep learning, a sub-mechanism of machine intelligence (MI), is a computing process that enables machines to find patterns in data. Deep learning sifts through data and makes connections, identifying odd trends that would be unfindable by the human senses.

For the past few months, I have been researching on building an end to end speaker identification system with deep learning. The area of research I focused on is metric learning. Knowing almost nothing going in, I have significantly underestimated the effort required (surprise!). While the effort is ongoing, I have learnt much along and have some preliminary results I am excited to share. In this blog post, I will explain what metric learning is, what is speaker identification, how metric learning applies in this context, and share the lessons I learnt from applying those knowledge to build a speaker embedding function.

In this post, it is demonstrated how to use OpenCV 3.4.1 deep learning module with MobileNet-SSD network for object detection. The dnn module allows load pre-trained models from most populars deep learning frameworks, including Tensorflow, Caffe, Darknet, Torch. Besides MobileNet-SDD other architectures are compatible with OpenCV 3.4.1: This API is compatible with C and Python. In this section, We'll create the python script for object detection and it is explained, how to load our deep neural network with OpenCV 3.4? How to pass image to neural network?

The graphic above shows only a sampling of companiesin each category.

Singer laid out for me the thinking at Intel about A.I. in advance of the company's first-ever dedicated A.I. conference, which happens next month. With A.I., Intel faces a challenge: the dominant player by conventional wisdom is Nvidia (NVDA), whose graphics chips, or "GPUs," have been widely used by Alphabet's (GOOGL) Google and Facebook (FB) and all the other cloud companies to do much of the heavy lifting for machine learning and deep learning. In addition, Silicon Valley has cultivating numerous startups focused on dedicated A.I. chips. Advanced Micro Devices (AMD), Intel's rival in PC chips, is also pushing the use of GPUs for A.I., being the only other vendor of size in GPUs besides Nvidia. Going up against that, Intel has Xeon, and also field-programmable gate arrays, acquired with the purchase of Altera, and also more specialized A.I. chips of its own from the acquisition a couple years back of Nervana.

Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g. shifting resources from one case onto another to ensure this latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 16 real-life datasets originating from different industry domains.

Collaborative machine learning and related techniques such as distributed and federated learning allow multiple participants, each with his own training dataset, to build a joint model. Participants train local models and periodically exchange model parameters or gradient updates computed during the training. We demonstrate that the training data used by participants in collaborative learning is vulnerable to inference attacks. First, we show that an adversarial participant can infer the presence of exact data points in others' training data (i.e., membership inference). Then, we demonstrate that the adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. We evaluate the efficacy of our attacks on a variety of tasks, datasets, and learning configurations, and conclude with a discussion of possible defenses.