Artificial Intelligence

Artifical Intelligence

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Artificial Intelligence Training & Certification

45 Days
        AI (Artificial Intelligence)


Artificial intelligence (AI) is a research field that studies how to realize the intelligent human behaviors on a computer. The ultimate goal of AI is to make a computer that can learn, plan, and solve problems autonomously. Although AI has been studied for more than half a century, we still cannot make a computer that is as intelligent as a human in all aspects. However, we do have many successful applications. In some cases, the computer equipped with AI technology can be even more intelligent than us. The Deep Blue system which defeated the world chess champion is a well-known example. The main research topics in AI include problem-solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on. Of course, these topics are closely related with each other. For example, the knowledge acquired through learning can be used both for problem-solving and for reasoning. In fact, the skill for problem-solving itself should be acquired through learning. Also, methods for problem-solving are useful both for reasoning and planning. Further, both natural language understanding and computer vision can be solved using methods developed in the field of pattern recognition.


The Google authorized Android Development course is primarily designed for programmers who want to learn how to create mobile applications on the Android platform. As a part of this course, you will create widgets, Customize List view, Grid view, Spinners etc, create applications using audio, video and sqlite database and finally publish it on Google Play. This course will help you learn mobile app development from scratch and unlock new job opportunities for you in start-ups as well as large organizations. Master Android app development, learn how to set up Android Studio, understand Android architecture in detail, learn about integrating your mobile apps with Facebook, Twitter and other social media, Google Drive, Google Maps, SQLite and learn how to create and optimize app user experience.

Course Content

Artificial Intelligence will define the next generation of software solutions. This computer science course provides an overview of AI and explains how it can be used to build smart apps that help organizations be more efficient and enrich people’s lives. It uses a mix of engaging lectures and hands-on activities to help you take your first steps in the exciting field of AI. Discover how machine learning can be used to build predictive models for AI. Learn how software can be used to process, analyze, and extract meaning from natural language; and to process images and video to understand the world the way we do. Find out how to build intelligent bots that enable conversational communication between humans and AI systems.

you may have heard the buzz about neural networks and deep learning, and want to learn more. But when you learn about the technology from a textbook, many people find themselves overwhelmed by mathematical models and formulas. I certainly was. For people like me, there’s an awesome tool to help you grasp the idea of neural networks without any hard math: TensorFlow Playground, a web app written in JavaScript that lets you play with a real neural network running in your browser and click buttons and tweak parameters to see how it works.

Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing.

RNNs are used in deep learning and in the development of models that simulate the activity of neurons in the human brain. They are especially powerful in use cases in which context is critical to predicting an outcome and are distinct from other types of artificial neural networks because they use feedback loops to process a sequence of data that informs the final output, which can also be a sequence of data. These feedback loops allow information to persist; the effect is often described as memory. RNN use cases tend to be connected to language models in which knowing the next letter in a word or the next word in a sentence is predicated on the data that comes before it. A compelling experiment involves an RNN trained with the works of Shakespeare to produce Shakespeare-like prose — successfully. Writing by RNNs is a form of computational creativity. This simulation of human creativity is made possible by the AI’s understanding of grammar and semantics learned from its training set.

A restricted Boltzmann machine is an algorithm used for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. (For more concrete examples of how neural networks like RBMs can be employed, please see our page on use cases). Given their relative simplicity and historical importance, restricted Boltzmann machines are the first neural network we’ll tackle. In the paragraphs below, we describe in diagrams and plain language how they work. RBMs are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks.

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Web Designer & Developer Owlok Technology


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