Discover the responsible and ethical side of Artificial Intelligence at fr33s0ul.tech. Our blog covers the latest advancements and applications of AI in various industries, with a focus on transparency, accountability, and privacy. From students to researchers, this is your source for a humble and ethical perspective on the rapidly-growing world of AI.
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Adversarial machine learning Adversarial machine learning exploits how artificial intelligence algorithms work to disrupt the conduct of artificial intelligence algorithms. In the past years, adversarial machine learning has become an active zone of research as the job of AI continues to grow in vast numbers of the applications we use.
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Convolutional Neural Networks (CNNs) A Convolutional Neural Networks (CNNs) is a Deep Learning algorithm which can take in an input picture, appoint significance (learnable weights and biases) to different articles in the image and have the option to separate one from the other. The pre-processing required in a CNNs; it
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Dimensionality reduction is a set of machine learning-based indicative models. They are useful to execute data manipulation; it decreases the dimensionality of a dataset. It is instrumental in cases where the issue becomes intractable, and the quantity of variables increases, then dimensionality reduction leads to choosing significant variables. * Low variance
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Now that we covered a fair amount of the components and instruments involving in the AI systems, it is the time to talk a little about performance evaluation and its importance. Performance evaluation is an essential step in a methodological operation. Because after finishing the whole machine learning model, testing
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Artificial neural networks Artificial neural networks are one of the trending topics in nowadays, Applications based on this type of technology imitate the sophisticated functionality of the human brain, where neurons process information in parallel. Researchers have successfully proved the concept by creating prototypes and successful models, for example, pattern
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Unsupervised learning The distinction in unsupervised learning from supervised learning, as it may have surmised the data will not contain any labels, and the module should attempt prospects and learn without a teacher. Semi-supervised learning The semi-supervised learning algorithm is a combination of supervised learning and unsupervised. The algorithms run
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It is a type of machine learning algorithms that learns from a provided input/output data to predict an un-provided output from the same form of input data in training. Supervised learning is often in our daily life, for example, the algorithm that indicates how much time a driver will
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Cybersecurity field grew so fast that now the industry needs many cybersecurity professionals to defend and patch issues in networks and web applications before cybercriminals attempt to do it. Still, thankfully we have nowadays a lot of conferences and many people working together to make the internet a better internet.