Demystifying Deep Learning – Back to Basics

Deep learning is a learning scheme that approaches the learning problem by learning the underlying representations; too much of learning. I thats why its also called as representation learning.

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Deep learning is a learning scheme that approaches the learning problem by learning the underlying representations; too much of learning. I thats why its also called as representation learning.

The post Demystifying Deep Learning – Back to Basics appeared first on Vinod Sharma's Blog.

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Updating the Definition of ‘Data Scientist’ as Machine Learning Evolves

By Bernardo Lustosa, Partner, cofounder, and COO at ClearSale In the early days of machine learning, hiring good statisticians was the key challenge for AI projects. Now, machine learning has evolved from its early focus on statistics to more emphasis on computation. As the process of building algorithms has become simpler and the applications for AI […]

By Bernardo Lustosa, Partner, cofounder, and COO at ClearSale

In the early days of machine learning, hiring good statisticians was the key challenge for AI projects. Now, machine learning has evolved from its early focus on statistics to more emphasis on computation. As the process of building algorithms has become simpler and the applications for AI technology have grown, human resources professionals in AI face a new challenge. Not only are data scientists in short supply, but what makes a successful data scientist has changed.

Divergence between statistical models and neural networks

As recently as six years ago, there were minimal differences between statistical models (usually logistic regressions) and neural networks. The neural network had a slightly larger separation capacity (statistical performance) at the cost of being a black box. Since they had similar potential, the choice of whether to use a neural network or a statistical model was determined by the requirements of each scenario and by the type of professional available to create the algorithm.

More recently, though, neural networks have evolved to support many layers. This deep learning allows for, among other things, effective and novel exploitation of unstructured data such as text, voice, images, and videos. Increased processing capacity, image identifiers, simultaneous translators, text interpreters, and other innovations have set neural networks further apart from statistical models. With this evolution comes the need for data scientists with new skills.

Unchanging elements of building algorithms

Despite the changes in algorithm structures and capabilities, the process of constructing high-quality predictive models still follows a series of steps that hasn’t changed much. More important than the fit and method used is the ability to perform each step of this process efficiently and creatively.

Field interviews. Data scientists are not usually experts in the subject they are working on. Instead, they are experts on the accuracy and precision required to create the algorithms for various corporate or academic decision-making processes. However, the requirement today is that data scientists develop an understanding of the problem the algorithm was meant to solve, so interviews with subject matter experts focused on that particular problem are essential. Now, data scientists can work on neural networks that span a range of broad knowledge areas, from predicting the mortality of African butterflies to deciding when and where to publish advertising for seniors. This means that today’s data scientists must be able and eager to learn from experts on many subjects.

Understanding the problem. Each prediction hinges on a wealth of factors, all of which the data scientist must know about in order to understand the causal relationships among them. For example, to predict which applicants will default on their loans, the data scientist must know to ask questions such as:

  • Why do people default?
  • Are they planning to default when they apply?
  • Do defaulters have outsize debt relative to their income?
  • Is there fraud in the application process?
  • Is there sales pressure to apply for the loan?

These are some of the many questions to ask on this topic, and there is long lists of questions for every machine learning process. A data scientist who only wants to create algorithms without talking in depth with those involved in the phenomenon being explored will have a limited ability to create effective algorithms.

Identifying relevant information. As a data scientist sifts through the answers to these types of questions, he or she must also be skilled at picking out the information that may explain the phenomenon. A well-trained, inquisitive data scientist will also seek out related data online via search, crawler, and API to pinpoint the most relevant predictive factors.

Sampling. Statistical knowledge — on top of computational knowledge, experience, and judgment — matters for the definition of the response variable, the separation of the database, the certification of past data use, the separation of data between adjustment, validation and testing, and other sampling steps. However, the computational approach supports the use of the ever-larger databases that are required for the construction of complex algorithms. Therefore, both statistical and computational skill sets are a must for today’s data scientists.

Read the source article in VentureBeat.

Here are 22 Selected Top Papers on Deep Learning

By Asif Razzaq, Digital Health Business Strategist, cofounder MarkTechPost 1. Deep Learning, by Yann L., Yoshua B. & Geoffrey H. (2015) Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object […]

By Asif Razzaq, Digital Health Business Strategist, cofounder MarkTechPost

1. Deep Learning, by Yann L., Yoshua B. & Geoffrey H. (2015)

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics.

2. Visualizing and Understanding Convolutional Networks, by Matt Zeiler, Rob Fergus

The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery.

3. TensorFlow: a system for large-scale machine learning, by Martín A., Paul B., Jianmin C., Zhifeng C., Andy D. et al. (2016)

TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research.

4. Deep learning in neural networks, by Juergen Schmidhuber (2015)

This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

5. Human-level control through deep reinforcement learning, by Volodymyr M., Koray K., David S., Andrei A. R., Joel V et al (2015)

Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games.

6. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, by Christian S., Sergey I., Vincent V. & Alexander A A. (2017)

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. With an ensemble of three residual and one Inception-v4, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.

Read the source post at MarkTechPost.com.

Reinforcement Learning – Reward for Learning

Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there’s no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.

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Reinforcement learning can be understood by using the concepts of agents, environments, states, actions and rewards. This is an area of machine learning; where there's no answer key, but RL agent still has to decide how to act to perform its task. The agent is inspired by behaviourist psychology who decide how and what actions will taken in an environment to maximize some notion of cumulative reward.

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Scientists Develop AI System That Can See What You Are Thinking

A group of Japanese computer scientists has developed a new AI system that can visualize human thoughts. The technology can “see” the human thought or convert them into pictures. It’s always frightening to know that another person can read your thoughts. Imagine the case where technology can see what you are thinking! How It Works – […]

A group of Japanese computer scientists has developed a new AI system that can visualize human thoughts.

The technology can “see” the human thought or convert them into pictures. It’s always frightening to know that another person can read your thoughts. Imagine the case where technology can see what you are thinking!

How It Works – Functional MRI
 At the core of this technology lies the ability to scan the human brain. The scientists made use fMRI or Functional MRI to scan the brain over traditional MRI scan that can only monitor brain activity. fMRI, on the contrary, can track blood flow in the brain and even brain waves.

The system uses this data obtained from the scan to decide what the subject has been thinking. The resultant data is converted to image format, which is made possible by sending the data through a complex neural network that does the actual decoding.

But this technology couldn’t just grasp everything from the get-go. The machine has to be trained first to learn how the human brain works. It has to get used to tracking the blood flow.

Once the machine gets hold of the process, it begins projecting images that have a stark resemblance to what the subject was thinking. This was made possible only through employing multiple layers of DNN or Deep neural networks.

When DNN is tasked with processing the images, a DGN or Deep Generator Network is used to create images with more precision and accuracy. The differences in image creation with and without the DGN are vastly different.

The method of testing involves two steps. Firstly, the subject is shown an image, and then, the AI is made to recreate the images. The next part of the procedure makes the subject to visualize images in his/her mind. After which, AI system recreates the images in real time.

Read the source article in InterestingEngineering.com.

DATA – Blue Ocean Shift Strategy (Boss)

BOSS – Blue Ocean Shift Strategy can actually help and create vision to focus on areas such as AI, blockchain for education, health & agriculture, create ecosystems using BigData analytics and IoT. To capture a quick snapshot of this strategy, cer…

BOSS – Blue Ocean Shift Strategy can actually help and create vision to focus on areas such as AI, blockchain for education, health & agriculture, create ecosystems using BigData analytics and IoT. To capture a quick snapshot of this strategy, certainly, Big Data appears to be most effective and efficient driver for Blue Ocean Strategy. Based on a limited set....

2018 Year of Intelligence – Artificial & Augmentation

Year 2018 will be known as year of Artificial Intelligence and Intelligence Augmentation for sure.We used to think artificial intelligence was a silly sci-fi concept but when you really look into it, it seems like its been slowly encroaching into most …

Year 2018 will be known as year of Artificial Intelligence and Intelligence Augmentation for sure.We used to think artificial intelligence was a silly sci-fi concept but when you really look into it, it seems like its been slowly encroaching into most areas of everyday life! AI would become just a computer inside the robot or another software or a brain siting out of human body.

Demystifying AI, Machine Learning and Deep Learning

This was the first serious proposal in the philosophy of artificial intelligence, which can be explained as: a science developing technology to mimic humans to respond in a circumstance. In simple words AI involves machines that behave and think like h…

This was the first serious proposal in the philosophy of artificial intelligence, which can be explained as: a science developing technology to mimic humans to respond in a circumstance. In simple words AI involves machines that behave and think like humans i.e Algorithmic Thinking in general. Computers start simulating the brain’s sensation, action, interaction, perception and cognition abilities.

World Wide Data Wrestling

Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami like data flow industry of “Payments”. FinTech, InsureTech, MedTech are major data generating industries i.e massive group of factories. Acc…

Big data presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami like data flow industry of “Payments”. FinTech, InsureTech, MedTech are major data generating industries i.e massive group of factories. According to some data from Google it shows technology based innovative insurance companies

FinTech – Machine Learning and Recommenders

These terms are used to from BI Intelligence, To illustrate the various applications of AI in eCommerce and use case studies to show how this technology has benefited merchants/ecommerce service providers. Different consumers have varying, and often ve…

These terms are used to from BI Intelligence, To illustrate the various applications of AI in eCommerce and use case studies to show how this technology has benefited merchants/ecommerce service providers. Different consumers have varying, and often very specific, requirements for product, needs, expect performance, cost of consumption, silicon wafer thin kind of cost for best thing in mind and other parameters.