Category: Machine Learning

Neural networks

What are Neural Networks? | Strong and Jovial Plain Text

The human brain is an impressive feat of cognitive engineering, giving us the upper-hand when it comes to coming up with original ideas and concepts. We’ve even managed to create the wheel – something that not even our robot friends could do! This shows just how far we’ve come in terms of evolution – proving that humans are true masters of invention.

The Fundamentals Of Machine Learning

The main purpose of ML (machine learning) is to create an automatic data model for the purpose of analysis. Thus ML is to create a system that can learn from the data according to the algorithm used. The result can be found by mapping the output to the input or finding patterns/structures or learning by rewarding/punishing.

Top 5 Deep Learning Applications on Social Media For Businesses

How do top brands create content that is always appealing and filled with a sense of humor to go viral in no time?  Advanced tools help businesses understand their competitors and build new strategies to outrank them with better brand positioning. And how do social media platforms offer you intelligent recommendations?

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Machine Learning – Challenges of Supervised Machine Learning

Supervised machine learning through historic data sets can hunt for correct answers, and the algorithm’s task is to find them in the new data. It uses labelled data with input features and output labels. The program uses labelled samples to identify correlations between input and output data. Output labels in supervised learning are called the “supervisory signal”.

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Neural Networks in Physics: Bridging Computation and the Powerful Laws of the Universe

ANNs have found applications in various areas of physics, including computational physics, particle physics, and astrophysics. In computational physics, ANNs are used to model and simulate complex physical systems, allowing researchers to understand and predict their behavior. ANNs have been employed in analyzing large datasets from particle physics experiments, aiding in the identification and classification of particles.

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Generative Adversarial Networks: The Art of Powerful AI Creativity

Generative Adversarial Networks (GANs) consist of two main components: a generator network and a discriminator network. The generator network generates synthetic data samples, while the discriminator network aims to distinguish between real and fake data. The two networks are trained simultaneously in an adversarial process, pushing each other to improve their performance. Here is a detailed explanation of the architecture and components of GANs.

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Demystifying Convolutional Neural Networks: The Physics of Machine Vision

CNNs are like the Sherlock Holmes of the digital world, equipped with powerful tools and methods to make sense of images in astonishing ways. They’re not just about pixels; they’re about teaching machines to understand and interpret our visual world. Deep dive into CNNs ignites curiosity even more because the universe of physics is bursting with mysteries waiting for brilliant minds like yours to uncover.

Machine Learning Models and L1 and L2 Regularization

rtificial Intelligence has changed the face of world technology. It is divided into multiple sub-fields as robotics, machine learning, natural language processing, and many more. All these fields had vastly contributed to the development of smartphones, computers, software, and other smart machines. Machine learning is a sub-field of AI which involves research and study based on computer algorithms.

2021 The Year of Transformers – Deep Learning

Transformers are a type of neural network architecture that has gained significant popularity due to their unwavering dedication to achieving optimal results in completing assigned tasks. Deep learning, which is widely recognized as a powerful tool, has significantly transformed the way we operate, proving to be both a lifesaver and a solution to disaster. Big players like OpenAI and DeepMind employ Transformers in their AlphaStar applications. …

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Machine Learning Transformation: Shaping the Future of Industries

Machine Learning Transformation involves a comprehensive and iterative process, combining data preparation, model development, evaluation, and deployment, with a strong focus on ethical and security considerations. The choice of techniques and technologies depends on the specific problem domain, data characteristics, and desired outcomes.

Neural Networks vs Deep Learning

Deep Learning Vs Neural Networks – Demystifying the Differences

Deep learning, also called a subset of machine learning which is a specialist with an extremely complex skillset in order to achieve far better results from the same data set. It purely on the basis of NI (Natural Intelligence) mechanics of the biological neuron system. It has a complex skill set because of methods it uses for training i.e. learning in deep learning is based on “learning data representations” rather than “task-specific algorithms.”

Machine Learning -Basic Terminologies in Context

The Machine Learning hype, too much information on the internet, and using ML terms by almost every tech show have actually created a “misinformation epidemic” of ML.  It is revolutionizing the way we do our business and what should be done to improve upon it. ML develops its own encompassing strategy from the experience it comes across over the period. Mathematics, statistics, programming, and the common sense of human beings are now part of integral components of machine learning.

Machine Learning – Changing Payments Security Landscape

Ministry of innovation adding lots of excitement and innovation thus increase in a fire about the security & privacy of transactions. Subscriber normally doesn’t ask too much in the low-value transaction but as it happens on a daily basis subscriber do get nervous and freak out more when they add their card or bank details on provided channels i.e. mobile app and web portal etc. Sadly data privacy is still in the back seat for many many providers.

Machine Learning an underlying technology for PaymentIntelligence

PaymentIntelligence – In today’s time payments are devoid of post hoc analysis, despite the fact that our money has transformed from being tangible to mere electronic data. Our contemporary milieu, as well as our daily lifestyles, spending patterns, and myriad activities, are becoming increasingly intricate. Incorporating machine learning and data science into real-time payment systems has the potential to yield intriguing insights into the behavior and culture of the payment industry, which may be identified as “payment intelligence.

Machine Learning

Top Machine Learning Algorithms – Data Scientist Basic Tool Kit

Learning Machine Learning skills is widely seen as a game-changing advantage for organizations, especially those with data-driven operations, as it has the potential to provide significant benefits. Nowadays, the most common term used to describe digital communication tools is social media platforms. The main aim of this written communication is to explicate and exemplify the prominent machine learning algorithms.