Free Coupon Discount – Feature Selection for Machine Learning, From beginner to advanced Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.

At BlackBerry's analyst summit this week, a great deal of time was spent on the company's secure QNX operating system, its IVY platform for software management on cars, and other tools and utilities designed for the next generation of personal transportation. This conversation can't happen soon enough. A growing concern of mine is that automobile companies don't yet seem to fully understand the risk they are taking with platforms that aren't secure enough for products tied to human transportation and safety. Having someone hack your phone or PC is bad, but having someone hack your car could be deadly. So when the industry is talking about putting apps in cars, safety and security should be a far higher priority for many of the automotive OEMs than it seems to be.

Every year, tropical hurricanes affect North and Central American wildlife and people. The ability to forecast hurricanes is essential in order to minimize the risks and vulnerabilities in North and Central America. Machine learning is a newly tool that has been applied to make predictions about different phenomena. We present an original framework utilizing Machine Learning with the purpose of developing models that give insights into the complex relationship between the land–atmosphere–ocean system and tropical hurricanes. We study the activity variations in each Atlantic hurricane category as tabulated and classified by NOAA from 1950 to 2021. By applying wavelet analysis, we find that category 2–4 hurricanes formed during the positive phase of the quasi-quinquennial oscillation. In addition, our wavelet analyses show that super Atlantic hurricanes of category 5 strength were formed only during the positive phase of the decadal oscillation. The patterns obtained for each Atlantic hurricane category, clustered historical hurricane records in high and null tropical hurricane activity seasons. Using the observational patterns obtained by wavelet analysis, we created a long-term probabilistic Bayesian Machine Learning forecast for each of the Atlantic hurricane categories. Our results imply that if all such natural activity patterns and the tendencies for Atlantic hurricanes continue and persist, the next groups of hurricanes over the Atlantic basin will begin between 2023 ± 1 and 2025 ± 1, 2023 ± 1 and 2025 ± 1, 2025 ± 1 and 2028 ± 1, 2026 ± 2 and 2031 ± 3, for hurricane strength categories 2 to 5, respectively. Our results further point out that in the case of the super hurricanes of the Atlantic of category 5, they develop in five geographic areas with hot deep waters that are rather very well defined: (I) the east coast of the United States, (II) the Northeast of Mexico, (III) the Caribbean Sea, (IV) the Central American coast, and (V) the north of the Greater Antilles.

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Contemplating possible future scenarios should be left to the field of philosophy, not to futurology – or so claims Luciano Floridi in this somewhat harsh but fair editor letter. Floridi examines so-called Artificial Intelligence (AI) winters and their impact on the development of AI. AI winters are periods where the hype for AI wanes and often the result of disillusionment when – inevitably – promises concerning AI applications fail to deliver. The drawbacks of hype for AI are twofold: on the one hand, it makes people more skeptical towards useful applications, and on the other hand alarmism blinds people to the actual risks associated with AI applications. Floridi mentions these instances of alarmism during his discussion of hype, seemingly categorizing alarmism as an instance of hype rather than a different variant of an exaggerated claim.

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In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.

Batch Normalization (BN or BatchNorm) is a technique used to normalize the layer inputs by re-centering and re-scaling. This is done by evaluating the mean and the standard deviation of each input channel (across the whole batch), then normalizing these inputs (check this video) and, finally, both a scaling and a shifting take place through two learnable parameters β and γ. Batch Normalization is quite effective but the real reasons behind this effectiveness remain unclear. Initially, as it was proposed by Sergey Ioffe and Christian Szegedy in their 2015 article, the purpose of BN was to mitigate the internal covariate shift (ICS), defined as "the change in the distribution of network activations due to the change in network parameters during training". In fact, a reason to scale inputs is to get stable training; unfortunately this may be true in the beginning but as the network trains and the weights move away from their initial values there is no guarantee of stability.

The company has raised $100 million in round C funding with the aim of becoming the "GitHub of machine learning". Inflection -- is an AI-first company aiming to redefine human-computer interaction. It is led by LinkedIn and DeepMind co-founders and was referenced in our Newsletter #68. The company has now raised $225 million in venture funding to use AI to help humans "talk" to computers. Unlearn -- aims to accelerate clinical trials by using AI, digital twins, and novel statistical methods to "enable smaller control groups while maintaining power and generating evidence suitable for supporting regulatory decisions".

The stock market is currently on the roughest losing streak since the start of the pandemic in 2020. The broad S&P 500 index is down 19% from its all-time high, putting it within a whisker of bear market territory. But the tech-centric Nasdaq-100 index is already there, with a loss of 28.3% since November 2021. While the investment picture might be nerve-wracking for many investors, history suggests down markets always eventually recover, so this might actually be a great time to put some money to work. Here's one fast-growing stock leveraging advanced technology, and it's worth considering because it's trading at an 88.9% discount to its all-time high, despite the company being highly profitable.