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Amesite » 5 Ways Museums are Driving Revenue Through Digital Transformation

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The future of museums is virtual. Moving collections online and creating virtual environments for patrons to experience a museum's offerings exponentially expands the levels of impact and reach a museum can have. Providing digital experiences of museum collections is a necessity for museums that want to build impact, prestige, and revenue. In fact, 98% of museums agree that their highest investment priorities include online platforms and digitalizing their collections [1]. It is clear that museums see the value of digital transformation; however, 69% of museums claimed to have a digital strategy [2], but only 23% of museums have digitalized parts of their collection [3].


Deep Learning: GANs and Variational Autoencoders

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Free Coupon Discount - Deep Learning: GANs and Variational Autoencoders, Generative Adversarial Networks and Variational Autoencoders in Python, Theano, and Tensorflow Created by Lazy Programmer Inc. Students also bought Natural Language Processing with Deep Learning in Python Advanced AI: Deep Reinforcement Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Ensemble Machine Learning in Python: Random Forest, AdaBoost Cutting-Edge AI: Deep Reinforcement Learning in Python Deep Learning: Advanced NLP and RNNs Preview this Udemy Course GET COUPON CODE Description Variational autoencoders and GANs have been 2 of the most interesting developments in deep learning and machine learning recently. Yann LeCun, a deep learning pioneer, has said that the most important development in recent years has been adversarial training, referring to GANs. GAN stands for generative adversarial network, where 2 neural networks compete with each other. Unsupervised learning means we're not trying to map input data to targets, we're just trying to learn the structure of that input data. Once we've learned that structure, we can do some pretty cool things.


Top 10 Mistakes You Should Avoid as a Data Science Beginner

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Data science is a huge success. Students from all over the world enroll in online courses and even master programs in data science. Data science is a highly competitive field, especially if you want to land one of the dream jobs at one of the top tech companies. You have the opportunity to be competitive in this field by being prepared. There are too many MOOCs and master programs, boot camps or blogs, as well as numerous data science academies. You may feel confused as a beginner. What course should I take?


Bayesian Machine Learning in Python: A/B Testing

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Step by Step Data Science with Python Roadmap

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Do you want to learn data science with python and looking for Data Science with Python Roadmap? If yes, then this article is for you. In this article, you will find a step-by-step roadmap to learn data science with python. Along with that, at each step, you will find resources to learn. So without any further ado, let's get started- So, you have chosen Python programming.


Natural Language Processing with Deep Learning in Python

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Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto], French [Auto] Created by Lazy Programmer Team, Lazy Programmer Inc. Comment Policy: Please write your comments that match the topic of this page post. Comments containing links will not be displayed until they are approved.


5 Tips to Boost Your Data Science Learning

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Many guides give you advice on how to get started in data science: which online courses to take, which projects to implement for your portfolio, and which skills to acquire. But what if you got started with your learning journey, and now you are somewhere in the middle and don't know where to go next? After finishing my Data Scientist nanodegree at Udacity, I was at that middle point. I had built a foundation in various data science topics -- ML, deep neural networks, NLP, recommendation systems, and more -- and my learning curve had been very steep. So I felt that simply taking another online course wouldn't yield as many "things learned per day."


A Beginner's Guide to Four Principles of Explainable Artificial Intelligence

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Artificial Intelligence is creating cutting-edge technologies for more efficient workflow in multiple industries across the world in this tech-driven era. There are machine learning and deep learning algorithms that are too complicated for people to understand besides AI engineers or related employees. Artificial Intelligence has generated self-explaining algorithms for stakeholders and partners to comprehend the entire process of transforming enormous complex sets of real-time data into meaningful in-depth insights. This is known as Explainable Artificial Intelligence or XAI in which the results of these solutions can be easily understood by humans. It helps AI designers to explain how AI machines have generated a specific kind of insight or outcome for businesses to thrive in the market. Multiple online courses and platforms are available for a better understanding of Explainable AI by designing interpretable and inclusive Artificial Intelligence.


Best computer science resource 2021: Top options

ZDNet

There are many online educational resources that tailor to helping computer science majors and professionals. Many computer science resources are available completely for free. You can leverage mobile apps, open online courses, websites, podcasts, and blogs to supplement computer science degree materials. Resources such as blogs and podcasts can also help with continuing education. It pays to keep abreast of industry news and discussion in the fast-moving world of computer technology.