April 11, 2019 Sexually violent predator Alan James released to home in Jacumba Hot Springs 00:00 00:00 spaceplay / pause qunload | stop ffullscreenshift + ←→slower / faster ↑↓volume mmute ←→seek . seek to previous 12… 6 seek to 10%, 20% … 60% XColor SettingsAaAaAaAaTextBackgroundOpacity SettingsTextOpaqueSemi-TransparentBackgroundSemi-TransparentOpaqueTransparentFont SettingsSize||TypeSerif MonospaceSerifSans Serif MonospaceSans SerifCasualCursiveSmallCapsResetSave SettingsSAN DIEGO (KUSI) – Sexually Violent Predator Alan James has been released from custody. This court order was granted despite opposition from victims, elected officials and community members.According to the District Attorney’s office, the court ruling ordered the release of James on or before March 25.The public is not told when the actual release takes place due to safety concerns for the convict, says the DA’s office.KUSI has confirmed James is out of custody and is now living at a home in Jacumba. He will continue to undergo outpatient therapy.James was convicted of multiple sex crimes on a child including rape and kidnapping.KUSI’s Ginger Jeffries has the follow up report. Ginger Jeffries, Categories: Local San Diego News FacebookTwitter Posted: April 11, 2019 Ginger Jeffries
Kolkata: The state Health department has issued a notification to Namita Biswas Memorial Eye Hospital in Garia’s Middle Town area, after it failed to renew registration.It has been learnt that the Health department asked the private hospital authorities not to admit patients, after it found some infrastructural lapses in the hospital. The hospital authorities, however, claimed that the Health department has given them verbal consent to continue normal functioning. A senior official of the hospital said that they were asked to stop functioning for two days as the registration was not renewed with the Health department. The hospital authorities, however, urged the health authorities for a relaxation.According to sources, the private eye hospital was running without a valid registration for quite some time. No hospital can operate without a valid registration from the Health department.A question appears here on how a private health establishment could admit patients when it had no valid clearance from the state Health department.
Korean Cultural Centre is up with their next show titled Secret Garden, a solo exhibition by Korean artist – Ahn Hyejo, that opens today. Secret Garden is a show that displays a garden located in the Changduk Palace of Joseon Dynasty well known Seoul’s attractions. This exhibition will showcase the character of Korean traditional garden culture. In this garden the artificial and natural beauties are harmonised with revealing relaxed, comfortable and Koreans’ esthetic emotions. The motive of Secret Garden is Korean traditional mask Tal reflecting Koreans’ sorrows and Korean traditional percussion quartet Samulnori. Also Read – ‘Playing Jojo was emotionally exhausting’Visual designer Ahn Hyejo will show tangible (intangible) Korean traditional inheritance which are reinterpreted by the tool of ‘design’. The art works regarding India national flower ‘lotus’ motives also are displayed and this is quite meaningful in the view of the relation between Korea and India.Besides these, Ahn Hyejo is showcasing the various experiences extending the scope of design and impression with connecting ceramics. These art works are expressed as like implicating mysterious land behind Secret Garden. Also Read – Leslie doing new comedy special with NetflixThe artist says, “I am living in India for 5 years. Many people have shown me much interesting regarding Korean culture and Korean life so it triggered me to design a graphic stories about Korean culture.”Some of Important Intangible Cultural Property show us common culture with India. Nong-Ak(Samulnori), Mask(Tal) dance express Koreans’ love for music and dance from the old historical times, like Indian peoples did and doing. Moreover Buddism and Lotus also have same important meaning in Korea and India as well.When: May 8 – June 5 Where: Exhibition Gallery, Korean Cultural CentreTiming: 10am – 5pm
April 10, 2017 3 min read Register Now » Growing a business sometimes requires thinking outside the box. Free Webinar | Sept. 9: The Entrepreneur’s Playbook for Going Global This story originally appeared on Engadget Uber has finally responded via the courts to Waymo’s allegation that it’s using the Alphabet company’s Lidar technology. The ride-hailing company called Waymo’s injunction motion to stop using technology that was allegedly misappropriated from Google servers a “misfire.” It also insisted that because it’s developing multi-lens LiDAR technology instead of the single-lens that Waymo uses, it’s not using stolen technology.Waymo’s lawsuit against Uber claims that former Google engineer, Anthony Levandowski stole 14,000 confidential documents pertaining to the search giant’s LiDAR tech and that Uber is using the technology found in those documents.After he left Google Levandowski went on to form the self-driving trucking company Otto that was acquired by Uber for $680 million. He currently leads Uber’s driverless car initiative.In the response to the injunction, Uber was forced to note that Waymo’s self-driving technology is currently ahead of the ride-hailing company’s. That’s not too surprising considering that Google started working on its self-driving tech in 2009, five years ahead of Uber. Uber also admitted that it’s still using off-the-shelf technology from LiDAR supplier Velodyne.Still Uber notes that it’s upcoming proprietary LiDAR is vastly different from Waymo’s.”Waymo’s injunction motion is a misfire: there is no evidence that any of the 14,000 files in question ever touched Uber’s servers and Waymo’s assertion that our multi-lens LiDAR is the same as their single-lens LiDAR is clearly false,” said Angela Padilla, Uber associate general counsel said in a statement. “If Waymo genuinely thought that Uber was using its secrets, it would not have waited more than five months to seek an injunction. Waymo doesn’t meet the high bar for an injunction, which would stifle independent innovation and competition.”The Alphabet company brought the lawsuit in response to an Uber email that was accidentally CC’d by a LiDAR component vendor to one of Google’s employees that contained an Uber circuit board that allegedly resembled Waymo’s proprietary design.In its response Uber states: “Waymo took one Uber schematic (inadvertently sent to a Waymo employee) and made several assumptions based on that one document to conclude that Uber’s LiDAR used a single-lens design”A Waymo spokesperson told Engadget: “Uber’s assertion that they’ve never touched the 14,000 stolen files is disingenuous at best, given their refusal to look in the most obvious place: the computers and devices owned by the head of their self-driving program. We’re asking the court to step in based on clear evidence that Uber is using, or plans to use, our trade secrets to develop their LiDAR technology, as seen in both circuit board blueprints and filings in the State of Nevada.”This trial could be spell trouble for Uber. If it’s found that one of its top executives did pilfer those documents and use the information within them to build Uber’s LiDAR technology, the financial judgement could cripple the ride-hailing company and give competitors like Lyft the opportunity to overtake it.
As a category manager, I manage the data science portfolio of product ideas for Packt Publishing, a leading tech publisher. In simple terms, I place informed bets on where to invest, what topics to publish on etc. While I have a decent idea of where the industry is heading and what data professionals are looking forward to learn and why etc, it is high time I walked in their shoes for a couple of reasons. Basically, I want to understand the reason behind Data Science being the ‘Sexiest job of the 21st century’, and if the role is really worth all the fame and fortune. In the process, I also wanted to explore the underlying difficulties, challenges and obstacles that every data scientist has had to endure at some point in his/her journey, or still does, maybe. The cherry on top, is that I get to use the skills I develop, to supercharge my success in my current role that is primarily insight-driven. This is the first of a series of posts on how I got started with Data Science. Today, I’m sharing my experience with devising a learning path and then gathering appropriate learning resources. Devising a learning path To understand the concepts of data science, I had to research a lot. There are tons and tons of resources out there, many of which are very good. Once you seperate the good from the rest, it can be quite intimidating to pick the options that suit you the best. Some of the primary questions that clouded my mind were: What should be my programming language of choice? R or Python? Or something else? What tools and frameworks do I need to learn? What about the statistics and mathematical aspects of machine learning? How essential are they? Two videos really helped me find the answers to the questions above: If you don’t want to spend a lot of your time mastering the art of data science, there’s a beautiful video on how to become a data scientist in six months What are the questions asked in a data science interview? What are the in-demand skills that you need to master in order to get a data science job? This video on 5 Tips For Getting a Data Science Job really is helpful. After a lot of research that included reading countless articles and blogs and discussions with experts, here is my learning plan: Learn Python Per the recently conducted Stack Overflow Developer Survey 2018, Python stood out as the most-wanted programming language, meaning the developers who do not use it yet want to learn it the most. As one of the most widely used general-purpose programming languages, Python finds large applications when it comes to data science. Naturally, you get attracted to the best option available, and Python was the one for me. The major reasons why I chose to learn Python over the other programming languages: Very easy to learn: Python is one of the easiest programming languages to learn. Not only is the syntax clean and easy to understand, even the most complex of data science tasks can be done in a few lines of Python code. Efficient libraries for Data Science: Python has a vast array of libraries suited for various data science tasks, from scraping data to visualizing and manipulating it. NumPy, SciPy, pandas, matplotlib, Seaborn are some of the libraries worth mentioning here. Python has terrific libraries for machine learning: Learning a framework or a library which makes machine learning easier to perform is very important. Python has libraries such as scikit-learn and Tensorflow that makes machine learning easier and a fun-to-do activity. To make the most of these libraries, it is important to understand the fundamentals of Python. My colleague and good friend Aaron has put out a list of top 7 Python programming books which helped as a brilliant starting point to understand the different resources out there to learn Python. The one book that stood out for me was Learn Python Programming – Second Edition – This is a very good book to start Python programming from scratch. There is also a neat skill-map present on Mapt, where you can progressively build up your knowledge of Python – right from the absolute basics to the most complex concepts. Another handy resource to learn the A-Z of Python is Complete Python Masterclass. This is a slightly long course, but it will take you from the absolute fundamentals to the most advanced aspects of Python programming. Task Status: Ongoing Learn the fundamentals of data manipulation After learning the fundamentals of Python programming, the plan is to head straight to the Python-based libraries for data manipulation, analysis and visualization. Some of the major ones are what we already discussed above, and the plan to learn them is in the following order: NumPy – Used primarily for numerical computing pandas – One of the most popular Python packages for data manipulation and analysis matplotlib – The go-to Python library for data visualization, rivaling the likes of R’s ggplot2 Seaborn – A data visualization library that runs on top of matplotlib used for creating visually appealing charts, plots and histograms Some very good resources to learn about all these libraries: Python Data Analysis Python for Data Science and Machine Learning – This is a very good course with a detailed coverage on the machine learning concepts. Something to learn later. The aim is to learn these libraries upto a fairly intermediate level, and be able to manipulate, analyze and visualize any kind of data, including missing, unstructured data and time-series data. Understand the fundamentals of statistics, linear algebra and probability In order to take a step further and enter into the foray of machine learning, the general consensus is to first understand the maths and statistics behind the concepts of machine learning. Implementing them in Python is relatively easier once you get the math right, and that is what I plan to do. I shortlisted some very good resources for this as well: Statistics for Machine Learning Stanford University – Machine Learning Course at Coursera Task Status: Ongoing Learn Machine Learning (Sounds odd I know) After understanding the math behind machine learning, the next step is to learn how to perform predictive modeling using popular machine learning algorithms such as linear regression, logistic regression, clustering, and more. Using real-world datasets, the plan is to learn the art of building state-of-the-art machine learning models using Python’s very own scikit-learn library, as well as the popular Tensorflow package. To learn how to do this, the courses I mentioned above should come in handy: Stanford University – Machine Learning Course at Coursera Python for Data Science and Machine Learning Python Machine Learning, Second Edition Task Status: To be started During the course of this journey, websites like Stack Overflow and Stack Exchange will be my best friends, along with the popular resources such as YouTube. As I start this journey, I plan to share my experiences and knowledge with you all. Do you think the learning path looks good? Is there anything else that I should include in my learning path? I would really love to hear your comments, suggestions and experiences. Stay tuned for the next post where I seek answers to questions such as ‘How much of Python should I learn in order to be comfortable with Data Science?’, ‘How much time should I devote per day or week to learn the concepts in Data Science?’ and much more.. Read more Why is data science important? 9 Data Science Myths Debunked 30 common data science terms explained