Intel recently released its BigDL project for distributed deep learning on Apache Spark. BigDL has native Spark integration, allowing it to leverage Spark during model training, prediction, and tuning. This blog post gives highlights of BigDL and a tutorial showing how to get started with BigDL on Databricks. BigDL is an open source deep learning library from Intel. Modeled after Torch, BigDL provides functionality both for low-level numeric computing and high-level neural networks.

A pair of computer scientists at the University of California, Berkeley developed an AI-based attack that targets speech-to-text systems. With their method, no matter what an audio file sounds like, the text output will be whatever the attacker wants it to be. This one is pretty cool, but it's also another entry for the "terrifying uses of AI" category. The team, Nicholas Carlini and Professor David Wagner, were able to trick Mozilla's popular DeepSpeech open-source speech-to-text system by, essentially, turning it on itself. Given any audio waveform, we can produce another that is over 99.9% similar, but transcribes as any phrase we choose (at a rate of up to 50 characters per second) … Our attack works with 100% success, regardless of the desired transcription, or initial source phrase being spoken.

I have never regretted enrolling in the Inspiring Leadership through Emotional Intelligence course. It has indeed been a course that has provided me with new knowledge, ideas, and a broader perspective relating to;life in general. How could I be in a position to understand emotional, social and cognitive intelligence and their applicability in my personal life, work, and relationship? Not to mention dealing with chronic stress as a leader and the need for renewal. Professor Boyatzis is such an intelligent professor.

Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. For automatic washing machine,we can write programs for the washing machine functionality. All the materials for this course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or take decisions as DIY investor. Learning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for DIY investors' quantitative trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating fund historical data for back-testing to achieve greater effectiveness.

Interest for the search term'data science,' as measured by Google, over the last five years. In the tech industry, new skills and roles emerge faster than traditional education can keep up with. A recent example is the field of data science and the associated profession, Data Scientist. The simplest definition of the data science field is the practice of collecting, analyzing, and interpreting data -- aided by technology. Most Computer Science degrees do not yet offer Data Science as a major and, as such, many Data Scientists are self-taught. For this reason, it is possible to become a Data Scientist without a formal degree This article will explore what it's like to be a Data Scientist, the skillset required, and how to acquire these skills using mostly free or cheap online resources.

About this course: Welcome to Course 2 - Sales Strategy - This course is designed to discuss the application of intelligence analysis in the sales planning process. And this approach contributes to integrating the sales planning process into the corporate strategy of the company because, in the strategy analysis and formulation process, we apply models, frameworks, tools, and techniques that also apply to the sales planning and management process. Therefore, the expected outcomes of this course focus on the transition from traditional to strategic sales planning, by discussing and applying the concepts recommended to support the development of the strategic guidelines. The concepts, models, tools, and techniques discussed and practiced during the course focus on the improvement of value creation from the sales function empowered by intelligence analysis, a process which typically applies in the strategy analysis front. The discussions go through how intelligence analysis can support the sales function, by providing methods to connect strategy to marketing and sales planning processes.

R is on the rise and showing itself as a powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. The course is structured in simple lessons so that the learning process feels like a step-by-step guide to plotting. We start by importing data in R from popular formats such as CSV and Excel tables.

Artificial Intelligence (AI) has stopped being just a thing of Sci-Fi novels and movies. From self-driving cars and grocery shopping without cash registers (Amazon Go), to algorithms that detect diseases and speech recognition that allows us to have conversations with robots (Apple's Siri, for example) artificial intelligence is everywhere. And the near future will have more and more of it. Perhaps AI is not spread into education as much as it is in other fields, but this doesn't mean the future's not bright. A flower that blooms later can become as beautiful -- if not even more beautiful -- than the others.

Pulling together deep learning workshops for a large number of students, however, can be a time consuming, error prone, and costly exercise. Furthermore, technical issues with the environment setup and compatibility problems during the workshops impede learning and cause student dissatisfaction. These workshops typically have participants bring their laptops and have them download and install new software. However, with the wide range of laptop platforms (Windows, Mac, Linux), numerous configurations, and version conflicts with existing software, workshops can become frustrating both for presenters and attendees. The RAM and disk space available on laptops and their lack of GPUs affect the types of hands-on labs that can be offered, as deep learning workshops benefit heavily from specialized hardware such as GPUs. An alternative is to build new cloud based custom VMs specifically for the training – this avoids compatibility issues but is quite time consuming and often not reusable based on our experience.