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Machine Learning


Apple acquires machine learning startup Inductiv Inc. to improve Siri data – 9to5Mac – IAM Network

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Apple has acquired the machine learning startup Inductiv Inc., according to a new report from Bloomberg. The startup had been developing technology that uses artificial intelligence to identify and correct errors in datasets. The report explains that the engineering team from Inductiv has joined Apple "in recent weeks" to work on several different projects including Siri, machine learning, and data science. Apple issued its standard statement regarding the acquisition, saying it "buys smaller technology companies from time to time and we generally do not discuss our purpose or plans." The startup was founded by professors from Stanford University, the University of Waterloo, and the University of Wisconsin.


Deep Learning Boosts Call Center Speech Recognition During the COVID-19 Crisis – IAM Network

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A business operation hard hit by COVID-19 is the call center. Industries ranging from airlines to retailers to financial institutions have been bombarded with calls--forcing them to put customers on hold for hours at a time or send them straight to voicemail. A recent study from Tethr of roughly 1 million customer service calls showed that in just two weeks, companies saw the percentage of calls scored as "difficult" double from 10 percent to more than 20 percent. Issues stemming from COVID-19--such as travel cancellations and gym membership disputes--have also raised customer anxiety, making call center representatives' jobs that much more challenging. Companies thinking about investing in speech recognition should consider a deep learning-based approach, and what to take into consideration before implementing it.


Data Science complete guide on Linear Algebra - DeepLearning

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Mathematical intuition required for Data Science and Machine Learning. The linear algebra intuition required to become a Data Scientist. Then, this course is for you. The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithms work, its limitations and its underlying assumptions.


Deep Learning A-Z : Hands-On Artificial Neural Networks

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Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.


Feature Visualization on Convolutional Neural Networks (Keras) DataStuff

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According to Wikipedia, apophenia is "the tendency to mistakenly perceive connections and meaning between unrelated things" . It is also used as "the human propensity to seek patterns in random information". Whether it's a scientist doing research in a lab, or a conspiracy theorist warning us about how "it's all connected", I guess people need to feel like we understand what's going on, even in the face of clearly random information. Deep Neural Networks are usually treated like "black boxes" due to their inscrutability compared to more transparent models, like XGboost or Explainable Boosted Machines. However, there is a way to interpret what each individual filter is doing in a Convolutional Neural Network, and which kinds of images it is learning to detect.


The Actual Difference Between Statistics and Machine Learning

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Contrary to popular belief, machine learning has been around for several decades. It was initially shunned due to its large computational requirements and the limitations of computing power present at the time. However, machine learning has seen a revival in recent years due to the preponderance of data stemming from the information explosion. So, if machine learning and statistics are synonymous with one another, why are we not seeing every statistics department in every university closing down or transitioning to being a'machine learning' department? Because they are not the same!


Council Post: The Temptations Of Artificial Intelligence Technology And The Price Of Admission

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If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models. As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.


When not to use machine learning or AI

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Imagine that you've just managed to get your hands on a dataset from a clinical trial. Pretend that these datapoints map out the relationship between the treatment day (input "feature") and the correct dosage of some miracle cure in milligrams (output "prediction") that a patient should receive for over the course of 60 days. Now imagine that you're treating a patient and it's day 2. What dose do you suggest we use? I really hope you answered "17mg" since this was definitely not supposed to be a trick question. Now, how would you build software to output the right doses on days 1–5?


How To Code Linear Regression From Scratch -- Quick & Easy!

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Here, we load the chocolate data into our program using pandas; we also drop two of the columns we won't be using in our calculation: competitorname and winpercent. Our y becomes the first column in the dataset which indicates if our specific sweet is chocolate (1) or not (0). The remaining columns are used as variables/features to predict our y and, thus, become our X. If you're confused about why we're doing with …[:, 0][:,np.newaxis] on line 5, this is to turn y into a column. We simply add a new dimension to convert the horizontal vector into a vertical column!


Convolutional Neural Networks (CNN or ConvNet) by Sunny Solanki

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The convolutional neural network is a type of artificial neural network which has proven giving very good results for visual imagery over the last few years. Over the years many version of convolutional neural network has been designed to solve many tasks as well as to win image net competitions. Any artificial neural network which uses the convolution layer in its architecture can be considered as ConvNet. ConvNets typically start with recognizing smaller patterns/objects in data and later on combines these patterns/objects further using more convolution layers to predict the whole object. Yann Lecun developed the first successful ConvNet by applying backpropagation to it during the 1990s called LeNet.