Hot Deep Learning Applications to Watch Analytics Insight

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Simulating human reasoning was the main reason Watson was introduced by IBM but now it has been broadened to include all other forms of AI. Much of the recent hype has been about machine learning that leads to predictive behavior and analysis for enterprises. Slowly, one of the most complex forms of AI, deep learning is also gaining momentum. The neurons in the human brains can connect to other neurons anyhow without any specific pattern. But neural networks using machine learning are a replication of the brain network and consist of more defined connections.


AI on the Edge

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The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.


AI / Deep Learning applications course – limited spaces for niche – personalised education

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The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful.


Enterprise Machine Learning in a Nutshell (Repeat)

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Machine learning enables computers to learn from large amounts of data without being explicitly programmed to do so. We can already see how machine learning gives rise to new intelligent applications, from self-driving cars to intelligent assistants on our smartphones.


Web Apps Can Be More Secure With Machine Learning

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But in its report, "How Machine Learning Will Strengthen the Web Application Security Testing Market," the think tank also points to a different trend when it comes to web application attacks: Insecure web applications cause the most data breaches. Quoting Verizon's "Data Breach Investigation Report (DBIR) for 2016," Frost and Sullivan noted that "Although attacks on web applications account for only 8 percent of overall reported incidents (whether they were successful or not), attacks on web applications accounted for over 40 percent of incidents resulting in a data breach, and were the single-biggest source of data loss." Furthermore, the percentage of data breaches that leveraged web application attacks increased rapidly--from 7 percent in 2015 to 40 percent in 2016. In the face of this trend, Frost and Sullivan's report recommends machine learning technology for web application security testing.