Instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems. Experience the remote live training by way of interactive and remote desktop led by a human being!
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Overview What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real world problems being solved with applications of intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems. Hands-on experience will be gained by building a basic search agent. Adversarial search will be examined through the creation of a game and an introduction to machine learning includes work on linear regression.
Artificial Intelligence is a vast field encompassing issues that require more space than a single book on the topic. With inspiration from multiple fields of knowledge such as neurology, psychology, philosophy and literature to say the least; artificial intelligence is an amalgamation of yet more refined subjects which are briefly described in this course.
Have you ever wanted to create artificial intelligence software (AI) but did not know where to start? Have you wanted to put this AI into a robot? If so Leaf is for you. Leaf is the AI software that starts you with a working AI framework that you add to and customize as you wish. It was put together by the Leaf Project, a group robot development program whose objective is to develop a robot platform that supports experiments with artificial intelligence, vision, voice recognition, navigation, etc.
Machine translation (MT) is a discipline within Natural language processing (NLP) or more broadly Artificial intelligence (AI). It is one of the oldest computationals tasks: the first algorithms for MT were run on the first computers alongside with algorithms for cryptography and physical simulations as early as in 1940s. Shortly after, MT was dominated by rule-based approach which was replaced in 1990s by statistical approach only to be replaced by neural network methods in 2010s. This course is a guide to the Machine Translation. It is intended for everybody interested in how machine translation works, its history, limitations, quality and the current trends in the field.
This course is organized as follows:
The objective of this course is to use an Artificial Neural Network to classify between photos of two male subjects. We extracted features of the subjects eyes, nose and mouth using Minimum Eigenvalue Algorithm to extract the corner points. Then, this information is reduced to a vector that will be used as an input for the Artificial Neural Network Training and Classification.
EffortlessAI. This is an IPython Notebook that contains a variety of tools designed to make initializing, querying, and training an AI code-free and very easy to do. In order to get started, simply launch the notebook and scroll down to the ipywidgets-based GUI. Make sure to run all cells first. That's it!
Engine for training and developing AI engine for games. Created with tensorflow support to easily configure and run
To know what some concepts mean
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, topology and rules. It is most commonly applied in artificial life, general game playing, and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game (i.e. whether one player won or lost) can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use gradient descent on a neural network with a fixed topology.
Pytorch NLP Multitask Learning - A Pytorch Multi-task Natural Learning Processing model is trained using AI Platform with a custom docker container.
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. This allows the model to exploit commonalities and differences across tasks, improving efficiency and prediction accuracy for task-specific models, compared to training the models separately. Typically, a multi-task model in the age of BERT works by having a shared BERT-style encoder transformer, and different task heads for each task. Since HuggingFace's Transformers has implementations for single-task models, but not modular task heads, a few library architectural changes are performed.
SUSI AI Desktop Client Susi AI is an intelligent Open Source personal assistant. It is capable of chat and voice interaction by using APIs to perform actions such as music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real time information. Additional functionalities can be added as console services using external APIs. Susi AI is able to answer questions and depending on the context will ask for additional information in order to perform the desired outcome. The core of the assistant is the Susi AI server that holds the "intelligence" and "personality" of Susi AI. The Android and web applications make use of the APIs to access information from a hosted server.
This course contains the official PyToMayrch implementation of Neural relational inference for interacting systems.
Abstract: Interacting systems are prevalent in nature, from dynamical systems in physics to complex societal dynamics. The interplay of components can give rise to complex behavior, which can often be explained using a simple model of the system's constituent parts. In this work, we introduce the neural relational inference (NRI) model: an unsupervised model that learns to infer interactions while simultaneously learning the dynamics purely from observational data. Our model takes the form of a variational auto-encoder, in which the latent code represents the underlying interaction graph and the reconstruction is based on graph neural networks. In experiments on simulated physical systems, we show that our NRI model can accurately recover ground-truth interactions in an unsupervised manner. We further demonstrate that we can find an interpretable structure and predict complex dynamics in real motion capture and sports tracking data.
Cranium is a portable, header-only, feedforward artificial neural network library written in vanilla C99.
It supports fully-connected networks of arbitrary depth and structure, and should be reasonably fast as it uses a matrix-based approach to calculations. It is particularly suitable for low-resource machines or environments in which additional dependencies cannot be installed.
Cranium supports CBLAS integration. Simply uncomment line 7 in matrix.h to enable the BLAS sgemm function for fast matrix multiplication.
Cross-entropy loss (classification)
Mean squared error (regression)
Batch Gradient Descent
Stochastic Gradient Descent
Mini-Batch Stochastic Gradient Descent
Learning rate annealing
Fan-in weight initialization
CBLAS support for fast matrix multiplication
This course presents the ideas of Artificial Intelligence and Machine learning. We'll examine AI types and assignments, and AI calculations. You'll investigate Python as a famous programming language for AI arrangements, including utilizing some logical biological system bundles which will help you actualize AI.
Then, this course presents the AI devices accessible in Microsoft Azure. We'll survey normalized ways to deal with information investigation and you'll get explicit direction on Microsoft's Team Data Science Approach. As you experience the course, we'll acquaint you with Microsoft's pre-prepared and overseen AI offered as REST API's in their set-up of intellectual administrations. We'll execute arrangements utilizing the PC vision API and the facial acknowledgment API, and we'll do slant examination by calling the common language administration.
Utilizing the Azure Machine Learning Service you'll make and utilize an Azure Machine Learning Worksace.Then you'll prepare your own model, and you'll convey and test your model in the cloud. All through the course you will perform involved activities to rehearse your new AI abilities. Before the finish of this course, you will have the option to make, actualize and send AI models.
Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in the US set up a Fraud Prevention Taskforce to counter the unauthorized use of debit cards. Programs like Kasisto and Moneystream are using AI in financial services.
Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place. In August 2001, robots beat humans in a simulated financial trading competition. AI has also reduced fraud and financial crimes by monitoring the behavioral patterns of users for any abnormal changes or anomalies.
The use of AI machines in the market in applications such as online trading and decision-making has changed major economic theories. For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient. Other theories where AI has had an impact include rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization, and counterfactual thinking.
Artificial intelligence (AI), is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labeled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".
Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."
Unity is a cross-platform game engine developed by Unity Technologies, first reported and delivered in June 2005 at Apple Inc.'s Worldwide Developers Conference as a Mac OS X-selective game engine. Starting in 2018, the engine had been reached out to help in excess of 25 stages. The engine can be utilized to create three-dimensional, two-dimensional, computer generated simulation, and increased reality games, too as simulations and different encounters. The motor has been embraced by enterprises outside video gaming, such as film, automotive, architecture, engineering, and construction.
Inside 2D games, Unity permits the importation of sprites and a high level 2D world renderer. For 3D games, Unity permits the determination of texture compression, mipmaps, and goal settings for every stage that the game motor backings, and offers help for bump mapping, reflection mapping, parallax mapping, screen-space ambient occlusion (SSAO), dynamic shadows using shadow maps, render-to-texture and full-screen post-handling impacts.
In the field of Artificial Intelligence (AI) learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Artificial Intelligence (AI) is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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