It holds the honor as the first feature film based on a Twitter thread, itself one of the first of its kind, if not the first. Assuredly it does.Įverything in "Zola" is heightened to mimic the extremity and shallowness of social media. She recognizes that's what Stefani will say about her when the honeymoon ends. On their car trip Stefani brays her way through a story about a "dookey-ass bitch with her nappy-ass hair" and says, "Get your ghetto ass up out my face! It's not my fault you nasty! It's not my fault I make more money than you!" Stefani's boyfriend and her roommate are in hysterics as they listen, but Zola is far from entertained. Once Zola lets the "sis" business slide, it's all downhill. This is how it starts, with an attempt to connect that transforms into minstrelsy. See how far this girl is willing to let her step. She keeps talking, but Stefani homes in on one word she won't let go: " Sis." This becomes her only answer to everything Zola's says: "Si-is! Si-is! Si-is!" Zola finishes her speech and grins uncomfortably at the rictus breaking across Stefani's face and the glowing eyes above it.īy the very nature of who she is, though, Stefani can never be exactly like Zola, or live her life or fully understand her problems. "Sis, why you taggin' me on your photos?" The scene cuts to the pair of them in the parking lot continuing to gossip, talking over each other. "Why you DMing me?" Zola says, as if recalling another interaction with someone lesser. New friendship always feels euphoric and looks beautiful. "Oh God!" she says excitedly, slapping Zola on the thigh, a call Zola responds to with as similar enthusiasm. Bravo films that moment to look like Stefani gets her, like they're on the same page. "Same b***h that wanna smile in your face be the same b***h that gonna come for you later," Zola gleefully tells Stefani during their first club outing together, the only good one they'll have. Harris are speaking expressly to Black women in these small moments and others that seem innocuous without a close read what's happening. Director Janicza Bravo and her co-writer Jeremy O. Their arrival in Florida is where the story gets messy.īlack women, however, may clock Stefani for what she is the moment she opens her mouth and spews a thick blaccent that doubles as the distant clacking of trouble's heels, strutting straight in Zola's direction. Zola hesitates, but ultimately joins Stefani, her boyfriend and Stefani's "roommate" for the car trip down from Detroit. The next day Stefani hits up Zola and invites her to join her on a weekend in Tampa, luring her with the promise of making thousands in tips for dancing. Not long after they pull a shift together at a local club, where we watch them laugh together in the parking lot. The two hit it off immediately, connecting over a mutual affinity for pole dancing. It begins in a relatively benign place, with Taylour Paige's Zola meeting a woman named Stefani (Riley Keough) during her shift as a restaurant server. Narratively and aesthetically speaking, this film has layers. Technically it's a drama, but that doesn't describe the whole picture either. " Zola," though blackheartedly funny at points, is not a comedy.
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This week, Amon has a chat with Sharlto Copley about BEAST (16:05), and then the team talk about Idris Elba going mano a mano with a lion in Baltasar Kormákur’s latest (36:18). We see how many of the requirements we check off on MR MALCOLM’S LIST (50:40), as we review Emma Holly Jones’s Regency comedy. If you’d like to join the conversation or suggest a Hot Take for the gang to discuss tweet us at us: you like the show do subscribe, leave a review and rate us too! Plus, in our HOT TAKE (01:17:07), with The Sandman, House of the Dragon, and Lord of the Rings: The Rings of Power all either released or on the verge of release, we discuss what show is best equipped to fill the void of Game of Thrones, or if pursuing that goal is folly.Īnd we’re doing a live podcast at the London Podcast Festival on Thursday September 15! Book your tickets here: And Penélope Cruz and Antonio Banderas turn the lens on themselves in filmmaking satire OFFICIAL COMPETITION (01:06:04). This week… we make a special announcement about our first live podcast next month! Then Amon chats to Dan Trachtenberg about his latest directorial effort, PREY (12:20), and the pod team review the new Disney+ (grumble) thriller (32:07) then it’s all aboard David Leitch’s BULLET TRAIN (46:48) – next stop, murder and mayhem in this all-star action vehicle and a 15-year friendship carries a Palestinian refugee’s dreams of seeing her homeland again in documentary FADIA’S TREE (01:06:42). Plus, in our “HOT TAKE” (01:16:56), we try our best to figure out what the hell is going on at Warner Bros. In episode 33 of the Fade to Black podcast, the gauntlet has been thrown down for a trial by combat in The Last Duel (31:43), Ridley Scott’s French historical drama starring Adam Driver, Matt Damon, Jodie Comer and Ben Affleck. Alrighty, have now witnessed a couple eps of DinoSquad and can safely say.that Im sitting in the. The killing continues in Halloween Kills (47:03), and Ron’s Gone Wrong (59:12) in Disney’s latest animation about one boy and his robot. The bromantic superhero movie Venom returns to face a new symbiotic threat in Let There be Carnage (01:19:58), and for this week’s ‘Hot Take’ (01:36:01) we dissect that Venom: Let There Be Carnage end credit scene so be prepared for spoilers!Ĭlarisse also speaks with The Beta Test’s star and filmmaker Jim Cummings (11:24), and Ron’s Gone Wrong co-director and co-writers Sarah Smith & Peter Baynham (59:33). Select the volume that Plex should be installed on (you may have completed this step with a prior Package Center install already), then select Next.ģ. Open Synology DSM 7 and navigate to the Package Center, then search for Plex and install it. We will look at how to install Plex on a Synology NAS below using Synology DSM 7.ġ. However, the package has been updated on the Package Center and can now be installed that way! Prior to January of 2023, the process on how to install Plex on a Synology NAS required you to manually download and install the Plex package from Plex’s website. Conclusion – How to Install Plex on a Synology NAS
What I discovered? I'm a Big Fan of waterproof makeup, and now I'm forever on the mission to keep you updated on all the latest and greatest formulas. So, I handpicked the top-rated waterproof makeup products on YouTube and Reddit (where the reviews are always brutal and honest) and put them to the very wet test by submerging my face in a bowl full of water. But before I radically altered my routine, I needed to be convinced that these formulas would really work. And, honestly, same.īut during a particularly sweaty summer, when I spent every bathroom break adjusting my sad, smudgy eyeliner and tapping the concealer out of my creases, I wondered if there really was something to the whole waterproof thing. Heavy makeup coverage on top of freckles that have not been color-corrected can look odd, but a color-correcting primer will help neutralize the colors (in addition. People do tend to have deeper, pressure-induced lines on the side they sleep on. Because unless you're an Olympic swimmer lookin' for a major face beat, or you're in the throes of Coachella, you’re probably happy with your regular formulas for everyday life. Dermatologist Ellen Marmur of Marmur Medical in New York City says that using the patches nightly could actually make a difference over time, helping to prevent repetitive motions (like scrunching or raising your eyebrows) that make wrinkles worse. This is meant to give you a lifted look around your eyes and cheeks. You place the tape near each of your temples and then you pull and tie the strings behind your head. You take two pieces of tape with strings attached, he explains. Similarly, we can apply the same method to create deep scars on other body parts like hands, palms, and feet.You've probably tried a water-resistant mascara or a long-lasting lipstick before, but have you ever worn a full face of waterproof makeup.in the water? My guess is no. The first one involves using tape and a string. In the previous tutorial, we discussed creating fake cuts on faces for Halloween makeup. Then take red blood gel and apply it inside the cuts, it will give a shiny red effect on the scar.Ĭheck out the video tutorial Creating fake cuts on other body parts Use red eye shadow around the cuts to make the wound look irritated. Don’t overthink the placement, just carelessly dot. This particular pencil is key because it’s got a super-tiny, precise tip that will subtly create freckles from scratch and not look cartoonish. Use Brow Ultra Slim Defining Eyebrow Pencil in Soft Brown to gently dot on freckles. Once the wax dries, use dark red face paint inside the cut with the help of a brush. HOW TO DO FAKE FRECKLES WITH MAKEUP STEP 1 - DOT ON PRODUCT. You can put liquid latex around the open wounds to make them stronger, it will last longer.Ĥ. Now use a spatula or a small knife-like tool to make a cut on the wax.ģ. With the help of the fingers, shape the wax and make it smooth to blend it properly with your skin. Make sure to use some oil in your fingers to avoid the wax sticking to your fingers while shaping the wound.Ģ. Next, you need to take some modeling wax and shape it like a long cut of a wound and apply it over your skin. Grab a lip liner to make the outline of the scar on the area of your face. Makeup instructions to make fake cuts and woundsġ. To make those fake wounds you need the following makeup supplies. It can be a forehead or a long scar on the cheeks. Image Credit: Julita Jędrych (Instagram)įirst, you need to identify, where you want to create scars on your face. Let’s start How to make fake wounds for Halloween makeup? With some basic Halloween makeup information, you can add your creative touch to make your fake wounds look more realistic. Cute & Funny Halloween Costumes for Kids. I’ll show you the step-by-step procedure in this fake wounds Halloween makeup tutorial. They are easy and simple to make by using regular makeup supplies. You don’t need to be an SFX artist to create those fake wounds. You might be thinking, doing creepy Halloween makeup is not my cup of tea. Please read the disclaimer.Īs Halloween is just around the corner, I’m sure you would love to terrify your friends with some fake wounds Halloween makeup ideas.ĭeep cut wounds and scars on your face will make your Halloween outfit more realistic, especially when you’re planning to become a zombie or a dead ghost at the Halloween party. Then you use the additional cable provided to connect one side of the splitter to the cable box, and the other side to the back of the cable modem. Setup instructions will probably direct you to unscrew the round coaxial cable that runs from the wall to the back of the cable box and attach the splitter. If you have DSL, plug in a DSL filter to the other jack, and then plug your phone into the end of the filter. Plug in your dial-up modem line or DSL cable into one open jack. Bottom: Snap this splitter into a phone jack in the wall. Then screw a cable to each of the two other connectors one goes back into the TV or cable box and the other goes into your cable modem. Top: Screw the coaxial cable from the wall into the single end of this connector. You can buy either of these for a few dollars at places like Radio Shack or computer stores. You usually sign up for DSL service through your phone company.įigure 1-2. One-eyed jacks are fine for cards, but splitters like these convert one cable or telephone connection into two so you can share your cable or phone line with your computer. DSL, on the other hand, uses your existing telephone lines to carry its signal. But this method is slow, expensive, and rare in residential areas.)Īs the name suggests, a cable modem ( Figure 1-1) uses your cable-TV company’s network of wires to pipe data into your house, right alongside Comedy Central and HBO. (In some remote areas, you can also get broadband satellite service. If you, a dial-up customer, have been paying for two phone lines just so you can talk and be online at the same time, you’ll actually save money withīroadband because you can cancel the second phone line.īroadband connections usually come in the form of aĭSL box (digital subscriber line). But most people take the easy road: they allow a representative from the phone company or cable company to come to their home or office to install the modem and configure the Mac or PC to use it.ĭSL services cost $30 to $40 a month. You can set up the equipment yourself to save a few bucks. There’s no 40-second wait while your modem screeches and dials. These connection methods hook you up to the Internet permanently, full time, so that you don’t waste time connecting or disconnecting-ever. If you have an older computer that has enough memory and hard drive space to meet the system requirements of Windows XP or Mac OS X, though, you can upgrade the old box with a new system and still see the best of the Web without buying a whole new machine. So Web browsers-and the software that lets them display video, animations, and other visual goodies-may be out-of-date and incapable of showing you everything that’s online. That’s because Apple and Microsoft stop updating their software when they move on to newer versions. Truth is, an old Mac or PC may not let you see all that the Web has to offer. That’s because fresh new operating systems like Windows XP and Mac OS X have built-in helper software designed to quickly guide your computer online.īut what if you already have a perfectly good four-or five-year-old model that works just fine for word processing or playing solitaire? In general, the newer the computer, the easier it’s probably going to be to get it connected to the Internet. Getting yourself that sort or gear, you need a computer.Ĭomputers are a lot cheaper than they used to be you can buy a new laptop these days for around $500. UP TO SPEED: The Most Essential Piece of GearĮquipment to get to the Internet, of course you’ve probably heard of modems, cable modems, and DSL. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points). Note: Recap of accuracy, precision, recall ¶Īccuracy measures how often the classifier makes the correct prediction. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, CharityML would identify no one as donors. It is always important to consider the naive prediction for your data, to help establish a benchmark for whether a model is performing well. This can greatly affect accuracy, since we could simply say "this person does not make more than $50,000" and generally be right, without ever looking at the data! Making such a statement would be called naive, since we have not considered any information to substantiate the claim. Looking at the distribution of classes (those who make at most $50,000, and those who make more), it's clear most individuals do not make more than $50,000. $$ F_$ score (or F-score for simplicity). We can use F-beta score as a metric that considers both precision and recall: Therefore, a model's ability to precisely predict those that make more than \$50,000 is more important than the model's ability to recall those individuals. Additionally, identifying someone that does not make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. It would seem that using accuracy as a metric for evaluating a particular model's performace would be appropriate. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.ĬharityML, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity.race: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other.relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.This project was set up and graded by Udacity (Machine Learning Engineer Nanodegree) The data we investigate here consists of small changes to the original dataset, such as removing the 'fnlwgt' feature and records with missing or ill-formatted entries. You can find the article by Ron Kohavi online. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article "Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid". The dataset for this project originates from the UCI Machine Learning Repository. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. This sort of task can arise in a non-profit setting, where organizations survive on donations. My goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. I will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. In this project, I will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. |
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