In a previous post, I described how decision tree algorithms work and demonstrated their use via the rpart library in R. Decision trees work by splitting a dataset recursively. That is, subsets arising from a split are further split until a predetermined termination criterion is reached. At each step, a split is made based on the independent variable that results in the largest possible reduction in heterogeneity of the dependent variable.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers.

This article was posted by SmileJet on Dev Battles. She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing.

What has changed in Machine Learning in the past 25 years? You may not care about this question. You may even not realize that Machine Learning as a technical and scientific field is older than 25 years. But I do care about this question. I care because I got a PhD in Machine Learning in 1990.

Process automation, machine learning, and IoT (Internet of Things) are playing a larger role in device lifecycle management, electronic recycling, and enterprise device trade-ins. Today, HYLA Mobile (a technology company focused on maximizing the residual value of mobile devices through optimized re-use working with carriers, OEMs, and other channels) continues to evaluate the ways emerging technologies and next generation solutions can support efficiencies, reduce manual labor, improve accuracy levels, and build intelligence into the company's overall operations.

Almost every action we do on the Internet or on mobile applications is recorded in files known as web logs. These logs can be very voluminous, providing a classic example of Big Data. Data science and Machine Learning algorithms can provide a way of extricating value from web logs. At the OVH Summit on the 11th of October, I presented a workshop on getting value out of web logs through Machine Learning with Dataiku DSS. In this article, I will run through the aspects of that presentation.

On Monday September 19th, Salesforce announced Salesforce Einstein, a full Artificial Intelligence (AI) Platform that includes what appears to be both Machine and Deep Learning Capabilities. They also announced a new Research Unit focused on AI. This blog is about the Salesforce Einstein Announcement and the profound implications for enterprises and vendors alike. The biggest thing about Einstein is that Salesforce is showing working prototypes. Salesforce provided a preview demonstration of Predictive Lead Scoring and it looked solid.

Real-time cell tower inspections by intelligent drone with deep learning capabilities demonstrated at GPU Technology Conference. One of the world's first automated inspections by an intelligent drone with deep learning capabilities was demonstrated at the GPU Technology Conference Europe by Aerialtronics, a Dutch manufacturer of technologically advanced drones, Neurala, a pioneer in deep learning software, and GPU-accelerated computing company NVIDIA. This new "intelligent drone" identifies objects and their condition in flight, which increases the efficiency and accuracy of documenting assets, lowering costs, and making it easier for frequent inspections. It adds to the use of commercial drones to help businesses access difficult and dangerous areas, such as cell towers and turbines. The resulting system can visually inspect a cell tower and recognise the equipment mounted on the mast.

So many works of film and fiction are hooked on the rise of artificial intelligence (AI) and what it might mean for humanity. They are often apocalyptic tales – Blade Runner, Alien, Terminator – where mankind comes off badly. But now that AI is being spearheaded by internet giants such as Google, Microsoft and Amazon, and our daily lives are increasingly affected by AI systems – chat bots, purchase prediction, news generation – what will really happen? In 2015 the UK media widely picked up on American media organisation NPR's calculator that could predict which jobs are susceptible to computerisation. The calculator, using research by the University of Oxford, said accountants have a 95% chance of losing their jobs as machines take over the number crunching and data analysis.

Israeli artificial intelligence startup Voyager Labs has emerged from stealth mode with customers, use cases, and $100 million in funding. Voyager Labs has raised the funds from prominent investors including Sir Ronald Cohen, Lloyd Dorfman, OCAPAC Holding Company, and Horizons Ventures to help finance the company's next generation solutions for the financial and e-commerce markets. The company has 90 employees at its R&D center in Hod Hasharon and offices in New York, Washington and London. Founded in 2012, Voyager Labs has developed an artificial intelligence engine that combines expert systems with deep-learning algorithms and is capable of extracting dynamic, real-time, and tailored insights into human behavior by analyzing massive amounts of publicly available unstructured data. Voyager Labs was founded by CEO Avi Korenblum, who served in the Israeli intelligence community for 20 years.