STEM


Mathematics for Data Science and Machine Learning using R

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From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way. What you'll learn Master the fundamental mathematical concepts required for Datas Science and Machine Learning Learn to implement mathematical concepts using R Master Linear alzebra, Calculus and Vector calculus from ground up Master R programming language Udemy Promo Coupon 75% off Discount Mathematics for Data Science and Machine Learning using R


Data Science Tutorial – Learn Data Science from experts – Intellipaat

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To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data.


Deep Learning for Single Cell Biology

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This is the second post in the series Deep Learning for Life Sciences. In the previous one, I showed how to use Deep Learning on Ancient DNA. Today it is time to talk about how Deep Learning can help Cell Biology to capture diversity and complexity of cell populations. Single Cell RNA sequencing (scRNAseq) revolutionized Life Sciences a few years ago by bringing an unprecedented resolution to study heterogeneity in cell populations. The impact was so dramatic that Science magazine announced scRNAseq technology as the Breakthrough of the Year 2018.


Learning Artificial Neural Networks by predicting visitor purchase intention

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As I am taking a course on Udemy on Deep Learning, I decided to put my knowledge to use and try to predict whether a visitor would make a purchase (generate revenue) or not. The dataset has been taken from UCI Machine Learning Repository. The first step is to import necessary libraries. Apart from the regular data science libraries including numpy, pandas and matplotlib, I import machine learning library sklearn and deep learning library keras. I will use keras to develop my Artificial Neural Network with tensorflow as the backend.


Five Machine Learning Paradoxes that will Change the Way You Think About Data

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Paradoxes are one of the marvels of human cognition that are hard to using math and statistics. Conceptually, a paradox is a statement that leads to an apparent self-contradictory conclusion based on the original premises of the problem. Even the best-known and well-documented paradoxes regularly fool domain experts as they fundamentally contradict common sense. As artificial intelligence(AI) looks to recreate human cognition, it's very common for machine learning models to encounter paradoxical patterns in the training data and arrive to conclusions that seem contradictory at first glance. Today, I would like to explore some of the famous paradoxes that are commonly found in machine learning models.


CPU vs GPU in Machine Learning

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Gino Baltazar currently operates as a data scientist/analyst in the intersection of technology, social impact, and sustainable investing with organizations such as ConservationX Labs, ShelterTech, ACTAI.global, Invest Impactly, and JPMorgan where he had served in mobile strategy.


Deep learning definition, algorithms, models, applications & advantages Science online

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Deep learning is also known as deep structured learning or hierarchical learning, It is part of a broader family of machine learning methods based on the layers used in artificial neural networks, Deep learning is a subset of the field of machine learning, which is a subfield of AI, Deep learning applications are used in industries from automated driving to medical devices. It is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation, Each successive layer uses the output from the previous layer as input, It can be learned in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners, It enables computational models which are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. It is a subfield of machine learning concerned with algorithms inspired by the structure & function of the brain called artificial neural networks, It can teach computers to do what comes naturally to humans: learn by example, Deep learning can be used in driverless cars, allowing them to recognize the stop sign, or to distinguish the pedestrian from the lamppost. The computer model learns to perform classification tasks from images, text, or sound, Deep learning models can achieve state of art accuracy, sometimes exceeding human-level performance, Models are trained by using a large set of labeled data & neural network architectures that have many layers. Neural networks are static & symbolic, They were inspired by information processing & distributed communication nodes in biological systems synaptic structures, they have many differences from the structural & functional properties of biological brains, that make them incompatible with the neurological evidence, while the biological brain of most living organisms is dynamic (plasticity) and analog.


Top Data Science and Machine Learning Methods Used in 2018, 2019

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Which Data Science / Machine Learning methods and algorithms did you use in 2018/2019 for a real-world application? This, in turn, mirrors the results of the 2017 poll, which found that the top 10 methods remained unchanged from the 2016 poll (although, again, they were in a different order). The average respondent used 7.4 methods/algorithms, which is in-line with both the 2017 and 2016 results. Below is a comparison of the top methods and algorithms in this year's poll with their 2017 shares. The most notable increases this year were found in the usage of various neural network technologies, including GANs, RNNs, CNNs, reinforcement learning, and vanilla deep neural networks.


Leveraging Nature and Nurture to Build Amazing AI SoCs

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Over the past decade, designers have developed silicon technologies that run advanced deep learning mathematics fast enough to explore and implement artificial intelligence (AI) applications such as object identification, voice and facial recognition, and more. Machine vision applications, which are now often more accurate than a human, are one of the key functions driving new system-on-chip (SoC) investments to satisfy the development of AI for everyday applications. Using convolutional neural networks (CNNs) and other deep learning algorithms in vision applications have made such an impact that AI capabilities within SoCs are becoming pervasive. It was summarized effectively by Semico's 2018 AI Report "...some level of AI function in literally every type of silicon is strong and gaining momentum." In addition to vision, deep learning is used to solve complex problems such as 5G implementation for cellular infrastructure and simplifying 5G operational tasks through the capability to configure, optimize and repair itself, known as Self Organizing Networks (SON).


Scientists teach computers fear--to make them better drivers

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NEW ORLEANS, LOUISIANA--Computers can master some tasks--like playing a game of Go--through trial and error. But what works for a game doesn't work for risky real-world tasks like driving a car, where "losing" might involve a high-speed collision. To drive safely, humans have an exquisite feedback system: our fight-or-flight response, in which physiological reactions like a rapid heart rate and sweaty palms signal "fear," and so keep us vigilant and, theoretically, out of trouble. Now, researchers at Microsoft are giving artificial intelligence (AI) programs a rough analog of anxiety to help them sense when they're pushing their luck. The scientists placed sensors on people's fingers to record pulse amplitude while they were in a driving simulator, as a measure of arousal.