Every new show and movie on Hulu February 2019

first_img Share your voice Now playing: Watch this: Tags 1:59 Netpicks February is nearly here and Hulu has got some fantastic shows and films coming. Since it’s almost Valentine’s Day, I must recommend you watch one of the greatest rom-coms ever written, Four Weddings and a Funeral. Or you can check out one of my other favorite romances, the Cher and Nicolas Cage masterpiece that is Moonstruck. If you’re not feeling the romantic vibe, and you’d prefer a good heist film, you may consider a The Thomas Crown Affair double feature. Hulu is picking up both the 1968 and 1999 versions. Comedy fans can check out both Wayne’s World and Wayne’s World 2 for another fun double feature option.  What’s streaming in February 2019center_img I also urge everyone to check out the documentary Three Identical Strangers, which was hands-down one of my favorite films from 2018. On the TV show front, Hulu is also getting the newest season of Archer on Feb. 25. Or check out the season of Legion on Feb. 3. Available on Hulu, February 2019Feb. 1Into The Dark: Down: Episode 5 PremiereRecord of Grancrest War: Complete Season 1A View to Kill (1985)The Animal (2001)Asterix & Obelix: Mission Cleopatre (2002)Bad Santa (2003)Barefoot (2014)The Big Lebowski (1998)The Bounty (1984)The Bourne Ultimatum (2007)Born on the Fourth of July (1989)Broadway Danny Rose (1984)Caddyshack (1980)Caddyshack II (1988)Capote (2005)Chaos (2005)Charlie and the Chocolate Factory (2005)Chasing Liberty (2004)Dazed and Confused (1993)Deep Blue Sea (1999)Delta Farce (2007)Dr. No (1962)Equilibrium (2002)Escape from Alcatraz (1979)Field of Dreams (1989)Flesh + Blood (1985)Foolish (1999)For Your Eyes Only (1981)Four Weddings and a Funeral (1994)Freedomland (2006)From Russia with Love (1964)Goldeneye (1995)Hairspray (1988)Hellboy II: The Golden Army (2008)How to Deal (2003)Kingpin (1996)Lara Croft: Tomb Raider (2001)Lars and the Real Girl (2007)Licence to Kill (1989)The Madness of King George (1994)Marathon Man (1976)Metro (1997)Mississippi Burning (1988)Moonraker (1979)Moonstruck (1987)Mortal Kombat (1995)Mortal Kombat Annihilation (1997)Mystic Pizza (1988)Next Day Air (2009)Old Fashioned (2014)On Her Majesty’s Secret Service (1969)The Portrait of a Lady (1996)The Purple Rose of Cairo (1985)The Quiet Ones (2014)Robin Hood: Prince of Thieves (1991)The Royal Tenenbaums (1997)Space Jam (1996)The Secret Garden (1993)Terminator 2: Judgement Day (1991)The Thomas Crown Affair (1999)The Thomas Crown Affair (1968)The Toybox (2018)Thelma & Louise (1991)Three Kings (1999)Thunderball (1965)Tomcats (2001)Tomorrow Never Dies (1997)Unforgettable (1996)Universal Soldier (1992)Untamed Heart (1993)Wayne’s World (1992)Wayne’s World 2 (1993)Wedding Crashers (2005)Wes Craven Presents: Dracula 2000 (2000)While You Were Sleeping (1995)Feb. 2Cabin Fever (2016)Pick of the Litter (2018)Feb. 3Legion: Complete Season 2 Feb. 4Saints & Sinners: Complete Seasons 1-3 Real Housewives of New York City: Complete Season 10Dog Days (2018)Experimenter (2015)Feb. 5Paid in Full (2002)Feb. 8PEN15: Complete Season 1Feb. 9The Preppie Connection (2016)Feb. 10The Song (2014)Feb. 11All Square (2018)Feb. 14False Flag: Complete Season 2Zac & Mia: Complete Season 2 Feb. 15Bondi Harvest: Complete Season 1Jamie’s Quick and Easy: Complete Seasons 1-2Next (2007)Feb. 16Proven Innocent: Series PremiereA Perfect Day (2016)Feb. 17The Party (2018)Feb. 18Elvis All-Star Tribute: Special The Sisters Brothers (2018)Feb. 20Stan Against Evil: Complete Season 3Feb. 23Death Wish (2018)Feb. 25Archer: Danger Island: Complete Season 9 Every Day (2018)The School (2018)Feb. 26The Enemy Within: Series PremiereThe Voice: Season 16Three Identical Strangers Feb. 27World of Dance: Season 3 Tickled (2016)Feb. 28Whiskey Cavalier: Season 1 Digging for Fire (2015)The Guilty (2018)Leaving Hulu in FebruaryFeb. 2812 Dates of Christmas (2011)A Mermaid’s Tale (2016)All Over the Guy (2001)Apollo 13 (1995)Bad Girls (1994)Bad Girls from Mars (1991)Basic Instinct (1992)Beetlejuice (1988)Best Seller (1987)Beverly Hills Vamp (1989)Blow Out (1981)Blue Jasmine (2013)Christmas Cupid (2010)Deja Vu (2006)Dr. Dolittle: Million Dollar Mutts (2007)Dream House Nightmare (2017)Dressed to Kill (1980)Exposed (2016)Hitman’s Run (1999)It’s Us (2016)Joey (1988)King of the Mountain (1981)Leaving Las Vegas (1995)Lethal Weapon (1987)Lethal Weapon 2 (1989)Lethal Weapon 3 (1992)Lethal Weapon 4 (1998)Line of Duty (2013)Living by the Gun (2011)Malena (2000)Manhattan Night (2016)Mansfield Park (1999)Message in a Bottle (1999)Miracle on 34th Street (1994)Mullholland Falls (1996)Operation Condor (1986)Operation Condor II: The Armour of the Gods (1991)Radio Days (1987)Ride (1998)Righteous Kill (2008)Rob Roy (1995)Silent Tongue (1993)Snow (2004)Snow 2: Brain Freeze (2008)Snowglobe (2007)Spy Game (2001)Switchback (1997)Teresa’s Tatoo (1994)Ulee’s Gold (1997)We are Marshall (2006)Wicker Park (2004)With a Friend like Harry (2000)For more information on what’s available to watch online, check out CNET.com/Netpicks or subscribe to the podcast — it’s free! And go to CNET sister site TVGuide.com to see what else is out in the world of streaming.Audio (weekly): RSS | iTunes | Google PlayVideo (monthly): iTunes (HD) | iTunes (HQ) | iTunes (SD) | RSS (HD) | RSS (HQ)| RSS (SD) TV and Movies Digital Media Post a comment 0 ABC Fox Hulu NBClast_img read more

Where love goes to Bollywood

first_imgMeet another author from the Mills&Boon India chapter. Mahi Jay, hard-core romanticist at heart, took her time to leave a regular, comfy profession behind. She picked up the pen and took love to Bollywood. Jay speaks to Millennium Post about writing, life and romanceTell us a little about yourself.?I am an independent share market trader married to the man of my dreams. Despite being total opposites in most ways, somehow those very differences seem to work beautifully for us.  Growing up, my world was filled with books. I used to make up my own endings and situations if those on the pages of the books did not work for me. But it didn’t occur to me until much later that I could actually create my own stories. One such story filled my mind and I just had to get it down. Which was when I saw an advertisement calling for aspiring authors for Harlequin. It seemed like an omen. I abandoned the story that I was working on and gave the contest a go and  luckily for me my short story worked. Also Read – ‘Playing Jojo was emotionally exhausting’How acquainted were you with the romance genre as a teenager? My first M&B was at 14 and I’d picked it up by accident. But after that there was no looking back. I devoured romance novels and their happy endings always gave me a high. For a while they were all I read. Until I discovered other genres and switched. But even now, my go to happy fix is a romance novel.Why choose a theme like a bollywood hero falling in love with a PR girl?In India everyone grows up on movies… So if i’m going to write a romance then I thought, why not about a bollywood hero? Someone larger than life. But it also gave me a chance to explore who he was as a person. His feelings, his foibles and his vulnerabilities. Normally it is the hero who rushes to rescue the damsel in distress but I created a strong independent heroine.     Which Bollywood heroes did you have a crush on that might have spilled over into the book??I was curious to see what kind of person a bollywood hero would be behind the screen persona. That curiosity served as my inspiration. Beyond that I didn’t model my hero against anyone in particular. My hero’s past shaped who he was and I went with that.last_img read more

Why TensorFlow always tops machine learning and artificial intelligence tool surveys

first_imgTensorFlow is an open source machine learning framework for carrying out high-performance numerical computations. It provides excellent architecture support which allows easy deployment of computations across a variety of platforms ranging from desktops to clusters of servers, mobiles, and edge devices. Have you ever thought, why TensorFlow has become so popular in such a short span of time? What made TensorFlow so special, that we seeing a huge surge of developers and researchers opting for the TensorFlow framework? Interestingly, when it comes to artificial intelligence frameworks showdown, you will find TensorFlow emerging as a clear winner most of the time. The major credit goes to the soaring popularity and contributions across various forums such as GitHub, Stack Overflow, and Quora. The fact is, TensorFlow is being used in over 6000 open source repositories showing their roots in many real-world research and applications. How TensorFlow came to be The library was developed by a group of researchers and engineers from the Google Brain team within Google AI organization. They wanted a library that provides strong support for machine learning and deep learning and advanced numerical computations across different scientific domains. Since the time Google open sourced its machine learning framework in 2015, TensorFlow has grown in popularity with more than 1500 projects mentions on GitHub. The constant updates made to the TensorFlow ecosystem is the real cherry on the cake. This has ensured all the new challenges developers and researchers face are addressed, thus easing the complex computations and providing newer features, promises, and performance improvements with the support of high-level APIs. By open sourcing the library, the Google research team have received all the benefits from a huge set of contributors outside their existing core team. Their idea was to make TensorFlow popular by open sourcing it, thus making sure all new research ideas are implemented in TensorFlow first allowing Google to productize those ideas. Read Also: 6 reasons why Google open sourced TensorFlow What makes TensorFlow different from the rest? With more and more research and real-life use cases going mainstream, we can see a big trend among programmers, and developers flocking towards the tool called TensorFlow. The popularity for TensorFlow is quite evident, with big names adopting TensorFlow for carrying out artificial intelligence tasks. Many popular companies such as NVIDIA, Twitter, Snapchat, Uber and more are using TensorFlow for all their major operations and research areas. On one hand, someone can make a case that TensorFlow’s popularity is based on its origins/legacy. TensorFlow being developed under the house of “Google” enjoys the reputation of the household name. There’s no doubt, TensorFlow has been better marketed than some of its competitors. Source: The Data Incubator However that’s not the full story. There are many other compelling reasons why small scale to large scale companies prefer using TensorFlow over other machine learning tools TensorFlow key functionalities TensorFlow provides an accessible and readable syntax which is essential for making these programming resources easier to use. The complex syntax is the last thing developers need to know given machine learning’s advanced nature. TensorFlow provides excellent functionalities and services when compared to other popular deep learning frameworks. These high-level operations are essential for carrying out complex parallel computations and for building advanced neural network models. TensorFlow is a low-level library which provides more flexibility. Thus you can define your own functionalities or services for your models. This is a very important parameter for researchers because it allows them to change the model based on changing user requirements. TensorFlow provides more network control. Thus allowing developers and researchers to understand how operations are implemented across the network. They can always keep track of new changes done over time. Distributed training The trend of distributed deep learning began in 2017, when Facebook released a paper showing a set of methods to reduce the training time of a convolutional neural network model. The test was done on RESNET-50 model on ImageNet dataset which took one hour to train instead of two weeks. 256 GPUs spread over 32 servers were used. This revolutionary test has open the gates for many research work which have massively reduced the experimentation time by running many tasks in parallel on multiple GPUs. Google’s distributed TensorFlow has allowed all the researchers and developers to scale out complex distributed training using in-built methods and operations that optimizes distributed deep learning among servers. . Google’s distributed TensorFlow engine which is part of the regular TensorFlow repo, works exceptionally well with the existing TensorFlow’s operations and functionalities. It has allowed exploring two of the most important distributed methods: Distribute the training time of a neural network model over many servers to reduce the training time. Searching for good hyperparameters by running parallel experiments over multiple servers. Google has given distributed TensorFlow engine the required power to steal the share of the market acquired by other distributed projects such as Microsoft’s CNTK, AMPLab’s SparkNet, and CaffeOnSpark. Even though the competition is tough, Google has still managed to become more popular when compared to the other alternatives in the market. From research to production Google has, in some ways, democratized deep learning., The key reason is TensorFlow’s high-level APIs making deep learning accessible to everyone. TensorFlow provides pre-built functions and advanced operations to ease the task of building different neural network models. It provides the required infrastructure and hardware which makes them one of the leading libraries used extensively by researchers and students in the deep learning domain. In addition to research tools, TensorFlow extends the services by bringing the model in production using TensorFlow Serving. It is specifically designed for production environments, which provides a flexible, high-performance serving system for machine learning models. It provides all the functionalities and operations which makes it easy to deploy new algorithms and experiments as per changing requirements and preferences. It provides an excellent feature of out-of-the-box integration with TensorFlow models which can be easily extended to serve other types of models and data. TensorFlow’s API is a complete package which is easier to use and read, plus provides helpful operators, debugging and monitoring tools, and deployment features. This has led to growing use of TensorFlow library as a complete package within the ecosystem by the emerging body of students, researchers, developers, production engineers from various fields who are gravitating towards artificial intelligence. There is a TensorFlow for web, mobile, edge, embedded and more TensorFlow provides a range of services and modules within their existing ecosystem making them as one of the ground-breaking end-to-end tools to provide state-of-the-art deep learning. TensorFlow.js for machine learning on the web JavaScript library for training and deploying machine learning models in the browser. This library provides flexible and intuitive APIs to build and train new and pre-existing models from scratch right in the browser or under Node.js. TensorFlow Lite for mobile and embedded ML It is a TensorFlow lightweight solution used for mobile and embedded devices. It is fast since it enables on-device machine learning inference with low latency. It supports hardware acceleration with the Android Neural Networks API. The future releases of TensorFlow Lite will bring more built-in operators, performance improvements, and will support more models to simplify the developer’s experience of bringing machine learning services within mobile devices. TensorFlow Hub for reusable machine learning A library which is used extensively to reuse machine learning models. Thus you can transfer learning by reusing parts of machine learning models. TensorBoard for visual debugging While training a complex neural network model, the computations you use in TensorFlow can be very confusing. TensorBoard makes it very easy to understand and debug your TensorFlow programs in the form of visualizations. It allows you to easily inspect and understand your TensorFlow runs and graphs. Sonnet Sonnet is a DeepMind library which is built on top of TensorFlow extensively used to build complex neural network models. All of this factors have made the TensorFlow library immensely appealing for building a wide spectrum of machine learning and deep learning projects. This tool has become a preferred choice for everyone from space research giant NASA and other confidential government agencies, to an impressive roster of private sector giants. Road Ahead for TensorFlow TensorFlow no doubt is better marketed compared to the other deep learning frameworks. The community appears to be moving very fast. In any given hour, there are approximately 10 people around the world contributing or improving the TensorFlow project on GitHub. TensorFlow dominates the field with the largest active community. It will be interesting to see what new advances TensorFlow and other utilities make possible for the future of our digital world. Continuing the recent trend of rapid updates, the TensorFlow team is making sure they address all the current and active challenges faced by the contributors and the developers while building machine learning and deep learning models. TensorFlow 2.0 will be a major update, we can expect the release candidate by next year early March. The preview version of this major milestone is expected to hit later this year. The major focus will be on ease of use, additional support for more platforms and languages, and eager execution will be the central feature of TensorFlow 2.0. This breakthrough version will add more functionalities and operations to handle current research areas such as reinforcement learning, GANs, building advanced neural network models more efficiently. Google will continue to invest and upgrade their existing TensorFlow ecosystem. According to Google’s CEO, Sundar Pichai “artificial intelligence is more important than electricity or fire.” TensorFlow is the solution they have come up with to bring artificial intelligence into reality and provide a stepping stone to revolutionize humankind. Read more The 5 biggest announcements from TensorFlow Developer Summit 2018 The Deep Learning Framework Showdown: TensorFlow vs CNTK Tensor Processing Unit (TPU) 3.0: Google’s answer to cloud-ready Artificial Intelligencelast_img read more