Mobile health (mHealth) is considered one of the most transformative drivers for health informatics delivery of ubiquitous medical applications. Machine learning has proven to be a powerful tool in classifying medical images for detecting various diseases. However, supervised machine learning requires a large amount of data to train the model, whose storage and processing pose considerable system requirements challenges for mobile applications. Therefore, many studies focus on… Continue
Added by AI on August 16, 2019 at 6:00am —
As we all know that artificial Intelligence is slowly slowly becoming the most important and most integral part for the small to big businesses. It helps the business from the purchase of a product or manufactures the products to deliver the product to the client or customers. Here we have come with a small analysis of big business Company named coca… Continue
Added by Priyank Soni on June 13, 2019 at 7:30pm —
This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artifacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique… Continue
Added by Sanjiban Sekhar Roy on March 1, 2019 at 11:30pm —
The rise in Machine Learning, Deep Learning and Artificial Intelligence technologies seems to breaking all barriers. All these technologies have the potential to spur innovation everywhere in the world. When it comes to deep learning, the technological advancement seem to be very uplifting. They have the power to help the companies perform better and more quickly. But, at the same time, there is certain amount of confusion surrounding these technologies as well.… Continue
Added by Samual Alister on December 7, 2018 at 5:00am —
Introduction: Deriving meaningful information out of heap of data is the minimal requirement for any establishment today for its survival & sustenance. There are many terminologies and buzz words related to this area that blurs the meaning leaving people confused, such as Bigdata, Data Ware house (DWH), BI analytics, AI (Artificial… Continue
Added by Niraj Kumar on September 22, 2018 at 5:57am —
I built a scenario for a hybrid machine learning infrastructure leveraging Apache Kafka as scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e.… Continue
Added by Kai Waehner on August 1, 2018 at 11:00pm —
Machine Learning / Deep Learning models can be used in different ways to do predictions. My preferred way is to deploy an analytic model directly into a stream processing application (like Kafka Streams or KSQL). You could e.g. use the … Continue
Added by Kai Waehner on July 8, 2018 at 4:26pm —
Many predict, and warn, that the Artificial Intelligence (AI) Revolution will change the world – and possibly the very essence of mankind. But society-changing revolutions are not new. History is full of such revolutions. What can we learn from those previous revolutions that might provide an indication as to how this AI revolution might play out?
We will examine two other revolutions – the Industrial Revolution and the Information Revolution… Continue
Added by Bill Schmarzo on June 26, 2018 at 3:30pm —
I had a new talk presented at "Codemotion Amsterdam 2018" this week. I discussed the relation of Apache Kafka and Machine Learning to build a Machine Learning infrastructure for extreme scale.
Long version of the title:
"Deep Learning at Extreme Scale (in the Cloud)
with the Apache Kafka Open Source Ecosystem - How to Build a Machine Learning Infrastructure with Kafka, Connect, Streams, KSQL, etc."
As always, I want to share the slide deck. The talk was… Continue
Added by Kai Waehner on May 8, 2018 at 9:30pm —
Deep learning is a sub-category within machine learning and artificial intelligence. It is inspired by and based on the model of the human brain to create artificial neural networks for machines. Deep learning will allow machines and devices to function in some ways as humans do.
Dr. Rodrigo Agundez of GoDataDriven is co-author of this article and very enthusiastic about the improvements that deep learning can offer. He’s been involved in the data science and analysis field for… Continue
Added by Ronald van Loon on May 7, 2018 at 11:00pm —
Clear up the confusion of how all-encompassing terms like artificial intelligence, machine learning, and deep learning differ.
Machine learning and artificial intelligence (AI) are all the rage these days — but with all the buzzwords swirling around them, it's easy to get lost and not see the difference between hype and reality. For example, just because an algorithm is used to calculate information doesn’t mean the label… Continue
Added by Venkatesan M on May 7, 2018 at 9:30pm —
The mantra for technology evolution has been to replace or minimize human assistance with machines. Traditionally human support systems are rapidly being replaced by machines & by automation. The dependence on human decision-making is shrinking fast. If we take a domestic cooking gas stove as an example, we now have the automated safety gas stove where the gas supply can be cut off completely by itself in case of gas leakage or mishandling, thus avoiding fatal accidents and… Continue
Added by Yuvan Asav on March 27, 2018 at 10:00pm —
I was recently asked to conduct a 2-hour workshop for the State of California Senior Legislators on the topic of “Big Data, Artificial Intelligence and Privacy.” Honored by the privilege of offering my perspective on these critical topics, I shared with my home-state legislators how significant opportunities await the state. I reviewed the once-in-a-generation opportunities awaiting the great State of California (“the State”), where decision makers could vastly…
Added by Bill Schmarzo on February 13, 2018 at 5:30am —
Organizations looking for justification to move beyond legacy reporting should review this little ditty from the healthcare industry:
The Institute of Medicine (IOM) estimates that the United States loses $750 billion annually to medical fraud, inefficiencies, and other siphons in the healthcare system…
Added by Bill Schmarzo on January 26, 2018 at 1:30pm —
Recently 2017 came to a glittering end and as we look back through the lens of technology, the winner was probably Artificial Intelligence aka AI. It received tremendous success as much as some of the industry leaders commented that 2017 was the ‘Year of AI’. This write-up is an attempt to collate the achievements under the academic and industry.
Starting off with academics, the sheer volume of papers published is increasing every year. To give you some statistics,… Continue
Added by Kinnar Kumar Sen on January 25, 2018 at 6:30am —
“Big Data is dead.” “Big Data is passé.”
“We no longer need Big Data; we need Machine Learning now.”
As we end 2017 and look forward to big (data) things in 2018, the most important lessons of 2017 – in fact, maybe the most important lesson going forward – is that Big Data is NOT a thing. Big Data isn’t about the volume, variety or velocity of data any more than car…
Added by Bill Schmarzo on January 20, 2018 at 5:30am —
I was recently a guest lecturer at the University of California Berkeley Extension in San Francisco. On a lovely Saturday afternoon, the classroom was crowded with students of all ages learning the tools of the modern economy. The craftspeople of the “Analytics Revolution” were busy learning new skills and tools that will prepare them for this Brave New World of analytics. I was blown away by their dedication!
As we teach the next generation, it’s important…
Added by Bill Schmarzo on January 19, 2018 at 5:00am —
In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression. All those methods come under… Continue
Added by Jobil Louis on January 16, 2018 at 8:00pm —
Most organizations’ IOT Strategy look like a game of ‘Twister’ with progress across important IOT capabilities such as architecture, technology, data, analytics and governance; variables comprising a series of random investments and decisions.…
Added by Bill Schmarzo on January 13, 2018 at 5:00am —
Libratus, the artificial intelligence (AI) engine designed by Professor Tuomas Sandholm at Carnegie Mellon University (CMU) and his graduate student Noam Brown has made an impression on Jason Les, one of the world’s top poker players. Poker News, the poker industry’s online news magazine, recently interviewed Les. A couple questions were telling when asked about which is a better name for his firstborn child and which is the more annoying opponent, Claudico or Libratus. For both questions,… Continue
Added by Ken Strandberg on January 9, 2018 at 3:00pm —