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Julia is a high-level, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. This means that developers can solve problems faster and more effectively. Julia is great for computational complex problems.Julia is used by more than 10,000 companies worldwide, including AstraZeneca, BlackRock, Google, Intel, Microsoft, Moderna, Pfizer, as well as NASA, the Federal Aviation Administration and the Federal Reserve Bank of New York. “The wonders of today’s world are created using digital models.Julia is the feminine form of the Roman family name, Julius, which derives from the name of the mythological Roman god, Jupiter. Jupiter presided over the heavens and light, and was responsible for the protection and laws of the Roman state. Julia can also mean “youthful,” a derivative of the Latin word “iuvenale.”
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Julia (2022 TV series)
| Julia | |
|---|---|
| Country of origin | United States |
| Original language | English |
| No. of seasons | 1 |
| No. of episodes | 8 |
Julia and Glen Edy are the parents of Canadian basketball player Zach Edey. After their son’s impressive form, the couple receives a lot of media attention. Here you will find everything you need to know about the Edy family.
Zach was born on May 14, 2002 and is shaping up to be one of the best youngsters. The basketball star has shown great potential and has earned enormous admiration from the public.
Zach was born in Canada to his parents Julia and Glen Edy. The 20-year-old grew up with ice hockey and basketball. He later gave up hockey to pursue a career in basketball.
Who Are Julia & Glen Edy?
Julia and Glen Edy are known to the public as the parents of talented Canadian basketball player Zach Edey. As Zach improves at an impressive rate, his family also finds himself in the media spotlight.
Julia and Glen are Canadian nationals and lived in their home country when Zach was born. They are parents to two children, Zach and Doug. Zach’s younger brother, Doug, is also a basketball player.
Zach grew up with immense love and support from both of his parents. They have always supported their son’s ambitions and dreams.
Zach Edey Parents: Julia & Glen Edy Age Gap
The age difference of Zach Edey’s parents is currently unknown. Despite the continued media interest in the wake of their son’s rise to fame, there are many unknown details about Julia and Glen.
They are eal parents for Zach’s growth and career development. They have two sons after a long marriage. Both Julia and Glen are actively involved in their children’s lives.
Since both Julia and Glen’s children are currently playing basketball, they could come from a sporting family history.
Zach Edey Family Details
Zach Edey comes from a small and happy family of four. The 20-year-old Canadian spent his childhood in his native country with his three family members.
The Edey family consisted of Julia. Glen and Doug Edey. The whereabouts and occupations of Julia and Glen are currently unknown. However, Doug follows in his brother’s footsteps and plays basketball.
Since both brothers are still young, they have plenty of time to improve their professional performance. The Edey family is very proud of Zach’s accomplishments.
Zach Edey Net Worth
Although Zach has just started his career, he is one of the most popular basketball players.
Since basketball attracts a large audience, the players are worth a lot.
Zach’s net worth is estimated to be around $100,000. But with so much of his career to watch for, his net worth is sure to improve greatly.
What is Julia used for?
Julia is a high-level, dynamic programming language, designed to give users the speed of C/C++ while remaining as easy to use as Python. This means that developers can solve problems faster and more effectively. Julia is great for computational complex problems.
Who Is Julia Wikipedia?
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Julia (2022 TV series)
| Julia | |
|---|---|
| Country of origin | United States |
| Original language | English |
| No. of seasons | 1 |
| No. of episodes | 8 |
What companies use Julia?
Julia is used by more than 10,000 companies worldwide, including AstraZeneca, BlackRock, Google, Intel, Microsoft, Moderna, Pfizer, as well as NASA, the Federal Aviation Administration and the Federal Reserve Bank of New York. “The wonders of today’s world are created using digital models.
Why is Julia called Julia?
Julia is the feminine form of the Roman family name, Julius, which derives from the name of the mythological Roman god, Jupiter. Jupiter presided over the heavens and light, and was responsible for the protection and laws of the Roman state. Julia can also mean “youthful,” a derivative of the Latin word “iuvenale.”
Who created Julia?
Why is Julia not popular?
The negatives that Julia users report are that it’s too slow to generate a first plot and has slow compile times. Also, there are complaints that packages aren’t mature enough – a key differentiator to the Python ecosystem – and that developers can’t generate self-contained binaries or libraries.
Where does Julia come from?
Julia is a girl’s name with Latin origins. The name Julia means “youthful” or “Jove’s child” and was once an imperial Roman name given to those born in the house of Julius Caesar. Despite its ancient roots, Julia is a modern and sophisticated yet simple name that rolls off the tongue.
What makes Julia unique?
The core Julia language supports graphics arrays and processing that you simply cannot get anywhere else. Having parallel computing tightly integrated into Julia with types and functions specifically designed to handle it is far more powerful than in most languages where this is an afterthought.
How old is the name Julia?
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Julia.
| Origin | |
|---|---|
| Word/name | Latin |
| Meaning | “of the gens Julia, a descendant of Julus“ |
| Other names |
Why was Julia invented?
“We were greedy for a language that is as fast as C++, with the high-level functionality of Python, R or Matlab. And so we created a single language — Julia — that allows us to do prototyping and production in the same language,” says Karpinski.
Does Julia replace Python?
Even though there are many benefits of using Julia over Python, Python will not disappear anytime soon. Here’s why: Julia is young; Python is mature. Julia was released publicly in 2012 while Python has been around for three decades.
Is Julia growing in programming?
As of January 1, 2022, Julia has been downloaded 35 million times, and it was downloaded three times more often in 2021 than in the last three years combined. The core language and its registered packages have amassed a total of 250k stars on Github, 13 times more than that sum six years ago.
What does Julia mean in the Bible?
matthew 11:28. Biblical name for Julia is “Gods peace for the weary“.
What Julia means in Spanish?
(informal) feminine noun (Mexico) Black Maria (Brit) (informal) ⧫ paddy wagon (US) (informal)
Do people use Julia?
Julia is now listed among the world’s 50 most-popular programming languages, according to one index. It’s still relatively niche — the 2019 index ranks Julia 50th, and Python 3rd — but it has a passionate user base.
Who is Julia? – 1986 CBS Sunday Movie
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The Julia Programming Language
The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more.
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Who Is Julia? (TV Movie 1986) – IMDb
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Julia Roberts | Biography, Movies, & Facts | Britannica
Julia Roberts, in full Julia Fiona Roberts, (born October 28, 1967, Smyrna, Georgia, U.S.), American actress whose deft performances in …
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Who are Julia & Lauren Burch? Twin sisters blowing up on …
Julia and Lauren Burch are twin sisters who have seen a huge rise in popularity on both Twitch and TikTok recently.
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Learn Julia For Beginners – The Future Programming Language of Data Science and Machine Learning Explained
Julia is a dynamic, high-level programming language designed to give users the speed of C/C++ while remaining as easy-to-use as Python. This means developers can solve problems faster and more effectively.
Julia is ideal for complex arithmetic tasks. Many of Julia’s early adopters focused on scientific fields such as chemistry, biology, and machine learning.
Despite this, Julia is a general-purpose language and can be used for tasks such as web development, game development, and more. Many consider Julia to be the next-generation language for machine learning and data science, including the Shopify CEO (among many others):
I fondly remember having to speak to @avibryant about it when it came out. I think it’s the natural Python replacement for ML/data. It’s amazing how much they’ve unlocked by going all-in on multiple sends. Macros are also well done (but no comptime). — tobi.eth (@tobi) December 24, 2021 Kudos to Julia from Tobi, CEO of Shopify
How to download Julia programming language ⤵️
There are two ways to run Julia: via a .jl file in an IDE like VS Code or command by command in Julia REPL (Read Evaluate Print Loop). In this guide we mainly use the Julia REPL. Before you can use both, you need to download Julia:
or just go to: https://julialang.org/downloads/
After installing Julia you should be able to launch it and see the following:
Julia 1.7 REPL after installation
Julia programming language basics for beginners
Before we can use Julia for all the fun things it was designed to do, like machine learning or data science, we first need to get some basic knowledge of the language.
We’ll start by going through variables, types, and conditions. Then we will talk about loops, functions and packages. Finally, we will address more advanced concepts such as structures and talk about additional learning resources.
This is going to be a whirlwind tour, so buckle up and get ready! It’s also worth noting that this tutorial assumes you are familiar with programming. If you don’t, check out this Introductory Julia course for nervous beginners.
An Introduction to Julia Variables and Types ⌨️
In Julia, variables are dynamically typed, which means you don’t have to specify the variable type when you create it.
julia > a = 10 # Create variable “a” and assign it the number 10 10 julia > a + 10 # Perform a basic math operation on “a” 20
(Note that in code snippets when you see julia> it means the code is running in the REPL.)
Just as we defined a variable above and assigned it an integer (whole number), we can do something similar for strings and other variable types as well:
julia> my_string = “Hello freeCodeCamp” # Define a string variable “Hello freeCodeCamp” julia> balance = 238.19 # Define a float variable 238.19
When creating variables in Julia, the variable name is always on the left and the value is always on the right after the equals sign. We can also create new variables based on the values of other variables:
julia> new_balance = balance + a 248.19
Here we can see that new_balance is now the sum (grand total) of 238.19 and 10. Also notice that the type of new_balance is a float (number with decimal precision), because if we add float and int together, we automatically get the higher precision type, in this case a float. We can confirm this by doing the following:
julia> typeof(new_balance) Float64
Due to the nature of dynamic typing, variables in Julia can also change type. This means holder_balance could be a float at one point and a string later:
julia> holder_balance = 100.34 100.34 julia> holder_balance = “The type has changed” “The type has changed” julia> typeof(holder_balance) String
You might also be excited to know that variable names in Julia are very flexible, in fact you can do something like:
julia > 😀 = 10 10 julia > 🥲 = -10 -10 julia > 😀 + 🥲 0
In addition to Emoji variable names, you can also use any other Unicode variable name, which is very helpful when trying to represent mathematical ideas. You can access these Unicode variables by doing a \ and then typing the name followed by the tab key:
julia> \sigma # press tab and it will set the symbol julia> σ = 10 # sigma equal to 10
Overall, the variable system in Julia is flexible and offers a variety of features that make writing Julia code easy while being expressive. If you want to learn more about variables in Julia, check out the Julia documentation: https://docs.julialang.org/en/v1/manual/variables/
How to write conditional statements in Julia 🔀
When programming, you often need to check certain conditions to ensure certain lines of code are executed. For example, if you’re writing a banking program, you might want to let someone withdraw money when the amount they’re trying to withdraw is less than the amount they have in their account.
Let’s look at a simple example of a conditional statement in Julia:
julia > bank balance = 4583.11 4583.11 julia > payout amount = 250 250 julia > if payout amount <= bank balance bank balance -= payout amount print("Payout ", payout amount, " from your account") end Payout of 250 from your account Here's a closer look at some parts of the if statement that may differ from other code you've seen: First, we use no : to denote the end of the line, and we don't need () around the statement either use around (although it is recommended). Next, instead of using {} or something similar to indicate the end of the condition, we use the end keyword. Just like we used the if statement, we can concatenate it with an else or an elseif: julia> withdrawal amount = 4600 4600 julia> if withdrawal amount <= bank balance bank balance -= withdrawal amount print("Withdrawal ", withdrawal amount, " from your account") else print("insufficient balance") end Insufficient balance You can read more about control flow and conditional expressions in the Julia documentation: https://docs.julialang.org/en/v1/manual/control-flow/#man-conditional-evaluation How to use loops in Julia 🔂 There are two main types of loops in Julia: a for loop and a while loop. As with other languages, the main difference is that in a for loop you iterate over a predefined number of elements, while in a while loop you iterate until a condition changes. Syntactically, the loops in Julia look very similar in structure to the if-conditions we just looked at: julia> greeting = [“hello”, “world”, “and”, “welcome”, “at”, “freeCodeCamp”] # define greeting, an array of strings 6-element Vector{String}: “hello” “world ” “and” “welcome” “to” “freeCodeCamp” julia> for word in greeting print(word, ” “) end Hello world and welcome to freeCodeCamp
In this example, we first defined a new type: a vector (also called an array). This array contains a series of strings that we have defined. The behavior is very similar to arrays in other languages, but it’s worth noting that arrays are mutable (which means you can change the number of elements in the array after you’ve created it).
If we look again at the structure of the for loop, you can see that we are looping through the greeting variable. On each iteration, we get a new word (in this case) from the array and assign it to a temporary variable word, which we then print out. You’ll notice that the structure of this loop is similar to the if statement, again using the end keyword.
Now that we’ve looked for loops, let’s switch gears and take a look at a while loop in Julia:
julia> while user_input != “End” print(“Enter input or Exit to exit: “) user_input = readline() # Prompt user for input end Enter input or Exit to Exit: hi Enter some input, or End to exit: test Enter some input, or End to exit: no Enter some input, or End to exit: end
For this while loop, let’s set it up to run indefinitely until the user types the word “end”. As you’ve seen a few times now, the structure of the loop should look familiar.
If you want to see more examples of loops in Julia, you can check out the section on loops in the Julia documentation: https://docs.julialang.org/en/v1/manual/control-flow/#man-loops
How to use functions in Julia
Functions are used to create multiple lines of code that are chained together and can be accessed by referencing a function name. First, let’s look at an example of a basic function:
julia> function believe() print(“Hello new Julia user!”) end believe (generic function with 1 method) julia> believe() Hello new Julia user!
Functions can also take arguments, just like in other languages:
julia> function believeuser(user_name) print(“Hello”, user_name, “, welcome to the Julia Community”) end believeuser (generic function with 1 method) julia> believeuser(“Logan”) Hi Logan, welcome to the Julia Community
In this example, we take an argument and then add its value to the expression. But what if we don’t get a string?
julia>greeuser(true) Hello true, welcome to the Julia community
Since we’re just printing in this case, the function keeps working even though it no longer takes a string and instead of taking a boolean (true or false) value. To prevent this, we can explicitly type the input arguments like this:
julia> function believeuser(user_name::String) print(“Hello”, user_name, “, welcome to the Julia Community”) end believeuser (generic function with 2 methods) julia> believeuser(“Logan”) Hello Logan, welcome to the Julia community
So now the function is defined to just take a string. Let’s test this to make sure we can only call the function with a string value:
julia>greeuser(true) Hello true, welcome to the Julia community
Wait a minute, why is this happening? We redefined the Greeuser function, it shouldn’t be true anymore.
What we’re witnessing here is one of Julia’s most powerful underlying features: Multiple Dispatch. Julia allows us to define functions with the same name and number of arguments, but accepting different types. This means we can create either generic or type-specific versions of functions, which greatly improves code readability since you don’t have to handle every scenario in a function.
We should quickly confirm that we have indeed defined both functions:
julia> Methods (Greetuser) # 2 Methods for the generic function “Greetuser”: [1] Greetuser (username::String) in Main at REPL[34]:1 [2] Greetuser (username) in Main at REPL[30] : 1
The built-in method function is perfect for this, telling us that we have defined two functions, the only difference being that one accepts any type and the other only a string.
It’s worth noting that since we have defined a specialized version that only accepts a string, every time we call the function with a string, the specialized version will be called. The more generic function is not called when passed a string.
Next, let’s talk about returning values from a function. In Julia you have two choices: you can use the explicit return keyword or you can choose to do it implicitly by using the last expression in the function as the return value like so:
julia> function sayhi() “this is a test” “hi” end of sayhi (generic function with 1 method) julia> sayhi() “hi”
In the example above, the string value “hi” is returned by the function because it is the last expression and there is no explicit return statement. You can also define the function as follows:
julia> function sayhi() “this is a test” return “hi” end of sayhi (generic function with 1 method) julia> sayhi() “hi”
In general, from a readability perspective, it makes sense to use the explicit return statement in case someone reading your code doesn’t know about the implicit return behavior in Julia functions.
Another useful feature is the ability to provide optional arguments:
julia> function sayhello(response=”hello”) return response end sayhello (generic function with 2 methods) julia> sayhello() “hello” julia> sayhello(“hi”) “hi”
In this example, we define response as an optional argument, so we can either allow the default behavior we defined to be used, or we can override it manually if needed. These examples only scratch the surface of what’s possible with functions in Julia. If you want to read more about all the cool things you can do visit: https://docs.julialang.org/en/v1/manual/functions/
How to use packages in Julia 📦
The Julia package manager and package ecosystem are some of the most important features of the language. I actually wrote an entire article about why it’s one of the most underrated features of the language.
Apart from that there are two ways to interact with packages in Julia: via the REPL or using the Pkg package. We will mainly focus on the REPL in this post as it is much easier to use in my experience.
After installing Julia, you can invoke the package manager from the REPL by typing ].
Pkg mode in the Julia REPL
Now that we’re in the package manager, there are a few things we usually want to do:
Add a package
Remove a package
Check what is already installed
If you want to see all possible commands in the REPL, just enter Pkg mode by typing ] and then ? followed by the Enter/Return key.
How to add Julia packages ➕
Let’s add our first package, Example.jl . To do this we can run:
(@v1.7) pkg> add example
which should give an output that looks something like this:
(@v1.7) pkg> Add example Update registry at ~/.julia/registries/General Update git repo https://github.com/JuliaRegistries/General.git Update registry at ~ /.julia /registries/JuliaPOMDP` Updating git repo `https://github.com/JuliaPOMDP/Registry` Resolving package versions… Installed example ─ v0.5.3 Updating `~/.julia/environments/v1. 7/Project.toml `[7876af07] + Example v0.5.3 Update `~/.julia/environments/v1.7/Manifest.toml` [7876af07] + Example v0.5.3 Precompile project… 1 dependency successful in 1 Second precompiled (69 already precompiled) (@v1.7) pkg>
For space reasons, I’ll skip further issues assuming you’ll follow me.
How to check package status in Julia 🔍
Now that we think we’ve installed a package, let’s double-check that it’s really there by typing status (or st for shorthand) into the package manager:
(@v1.7) pkg> st Status `~/.julia/environments/v1.7/Project.toml` [7876af07] Example v0.5.3 [587475ba] Flux v0.12.8
Here we can see that I have installed two packages, Flux and Example. It also gives me the path to the file managing my current environment (in this case global Julia v1.7) along with the package versions I have installed.
How to remove a Julia package 📛
If I want to remove a package from my running environment like Flux, I can just type remove Flux (or rm for short):
(@v1.7) pkg> rm Flux Update `~/.julia/environments/v1.7/Project.toml` [587475ba] – Flux v0.12.8
A brief status afterwards shows that this was successful:
(@v1.7) pkg> st status `~/.julia/environments/v1.7/Project.toml` [7876af07] example v0.5.3
We now know the basics of working with packages. But we committed a major programming crime by using our global packaging environment.
How to use Julia Packs 📦
Now that we’ve covered managing packages, let’s explore how to use them. Very simple, you just need to type using packageName to use a specific package you want. One of my favorite new features in Julia 1.7 (highlighted in this blog post) is shown below:
Image taken by the author
If you remember we removed the flux package and of course I forgot about that so I used it and loaded it by typing it with flux. The REPL automatically prompts me to install it via a simple “y/n” prompt. This is a small feature, but one that saves a tremendous amount of time and potential confusion.
It’s worth noting that there are two ways to access a package’s exported functions: via the using keyword and the import keyword. The big difference is that using automatically brings all functions into the current namespace (for which you can think of a large list of functions that Julia knows the definitions of), while import gives you access to all functions, but you have to prepend them to the Function with package name like: Flux.gradient() where flux is the name of the package and gradient() is the name of a function.
How to use structs in Julia?
Julia didn’t build any object-oriented programming (OOP) paradigms like classes into the language. However, similar to classes, structs in Julia can be used to create custom objects and types. Below we show a simple example:
julia> mutable struct Dogbreed::String paws::Int name::String weight::Float64 end julia> my_dog = dog(“Australian Shepard”, 4, “Indy”, 34.0) dog(“Australian Shepard”, 4, ” Indy”, 34.0) julia> my_dog.name “Indy”
In this example we define a structure to represent a dog. In the structure we define four attributes that make up the dog object. In the lines after that, we show the code to actually create a dog object and then access some of its attributes. Note that you don’t need to specify the types of the attributes, you could leave it more open-ended. For this example, we defined explicit types to emphasize this feature.
You’ll notice that similar to classes in Python (and other languages), we didn’t define an explicit constructor to create the dog object. However, we can define one if that would be useful:
julia> mutable structure dog breed::string paws::int name::string weight::float64 function dog(breed, name, weight, paws=4) new(breed, paws, name, weight) end end julia> new_dog = dog(“German Shepherd”, “Champ”, 46) dog(“German Shepherd”, 4, “Champ”, 46.0)
Here we have defined a constructor and used the special keyword new to create the object at the end of the function. You can also create getters and setters specifically for the dog object by doing the following:
julia> function get_name(dog_obj::dog) print(“The dog’s name is: “, dog_obj.name) end get_name (generic function with 1 method) julia> get_name(new_dog) The dog’s name is: Champ
In this example, the get_name function only accepts an object of type dog. If you try to pass anything else, you’ll get an error:
julia> get_name(“test”) ERROR: MethodError: no method matching get_name(::String) Closest candidates are: get_name(::dog) at REPL[61]:1 Stacktrace: [1] top-level scope @ REPL [63]:1
It’s worth noting that we also initially defined the structure as mutable so that we can change the field values after we create the object. Omit the mutable keyword if you want the object’s initial state to be preserved.
Structures in Julia not only allow us to create objects, we also define a user-defined type in the process:
julia> typeof(new_dog) dog
In general, structures are heavily used throughout the Julia ecosystem and you can learn more about them in the documentation: https://docs.julialang.org/en/v1/base/base/#struct
Additional Julia programming learning resources 📚
I hope this tutorial has helped you become familiar with many of the core ideas of the Julia language. Having said that, I know there are still gaps as this is an advanced but not comprehensive guide. To learn more about Julia, you can check out the Learning tab on the Julia website: https://julialang.org/learning/ for guided courses, YouTube videos, and supervised practice problems.
If you have any other questions or need help getting started with Julia, please feel free to contact me: https://twitter.com/OfficialLoganK
Julia (2022 TV series)
Julia is an American television series created by Daniel Goldfarb that premiered on HBO Max on March 31, 2022. It is based on the life of TV chef Julia Child. In May 2022, the series was renewed for a second season.
Cast and Characters[edit]
main [ edit ]
Recurring[ edit ]
Guest [edit]
Follow [edit]
No. Title Director Written by Original Release Date 1 “Omelette” Charles McDougall Daniel Goldfarb March 31, 2022 ( ) 2 “Coq au Vin” Charles McDougall Daniel Goldfarb March 31, 2022 ( ) 3 “Boeuf Bourguignon” Melanie Mayron Eboni Booth March 04/31. 2022 ( ) 4 “Petit Fours” Erica Dunton Natalia Temesgen 04/07/2022 ( ) 5 “Crepes Suzette” Jenée LaMarque Emily Bensinger 04/14/2022 ( ) 6 “Brot” Melanie Mayron Erica Lipez 04/21/2022 ( ) 7 “Foie Gras” Jenée LaMarque Daniel Goldfarb and Christopher Keyser April 28, 2022 ( ) 8 “Chocolate Souffle” Scott Ellis Christopher Keyser and Daniel Goldfarb May 5, 2022 ( )
production [edit]
The series began development at HBO Max in September 2019 after being greenlit to a pilot assignment, with Chris Keyser as showrunner and Joan Cusack on the verge of a deal to star in the title role.[2] By March 2020, Cusack had left the series and Sarah Lancashire was cast as Juliet. Supporting roles were added by Tom Hollander, Brittany Bradford, Bebe Neuwirth and Isabella Rossellini.[3] In March 2020, three days after the pilot started filming, production was suspended due to the COVID-19 pandemic.[4]
In September 2020, David Hyde Pierce was cast to replace Hollander, who left the project. In July 2021, Robert Joy, Erin Neufer, James Cromwell and Adriane Lenox were added to the cast.[6]
Filming of the pilot resumed in Boston in October 2020.[7] In January 2021, it was announced that the pilot – written by Daniel Goldfarb and directed by Charles McDougall – would join the series with an 8-episode order.[8] Filming concluded in September 2021.[9]
The series premiered on March 31, 2022, with the first three episodes available now and the rest debuting weekly through May 5. On May 4, 2022, HBO Max renewed the series for a second season.[11]
Reception [edit]
Review aggregator website Rotten Tomatoes reported an approval rating of 92% with an average rating of 7.80/10 based on 38 critic reviews. The site’s critics consensus reads, “Sarah Lancashire infuses Julia Child with infectious joie de vivre in this appetizing Valentine’s Day for the legendary chef.” “generally positive reviews”.[13]
Amy Amatangelo wrote for Paste that “the series is billed as comedy and there are definitely funny moments — especially when people literally get on the floor and help Julia film her show — but Julia doesn’t just explore how.” Child’s breakthrough was to show cooking on television, but how seminal she was for television production in general. […] Ultimately, the series hinges on Lancashire’s transformative performance. […] The series thrives on its strong female characters.”[14] David Cote, for The A.V. Club, gave Julia a B+ and wrote that “each roughly 45-minute episode walks a line between sentimental historical drama and high-pitched sitcom […] Lancashire carries the season on strong shoulders.”[15] Cote commented that the “season arc is pretty standard” and that “Julia’s closest thing to a villain is Feminine Mystique writer Betty Freidan (Tracee Chimo) , who chides Child at a public television gala for throwing things back. It’s a powerful moment that rocks Julia and complicates her status as a female liberator”.[15]
Vulture’s Kathryn VanArendonk called the show an “energizing, soothing hit of literacy porn: people who care and do their jobs well.” VanArendonk emphasized that “efforts to place Child in the cultural and historical context of her time make Julia a better show than it otherwise could have been. […] Julia has some flaws, but her good qualities outweigh those missteps”. 16] Caroline Framke for Variety compared Julia to The Marvelous Mrs. Maisel as both shows share producer Daniel Goldfarb; The show “does a remarkable job of recreating a specific part of upper-middle-class life anchored by a white woman whose oversized personality is matched with similarly remarkable talent”. Framke wrote, “Even though the series (from showrunner Chris Keyser) does identify more complex themes in the game – Julia’s heartbreak as she reaches menopause before having a child, her black producer Alice (Brittany Bradford) fighting to gain respect attaining what it deserves – it is mostly happily skipping along the (admittedly very appealing) surface”.[17]
Both Framke and VanArendonk praised Bradford’s performance as Alice, a fictional character based on actual producer Ruth Lockwood, but were critical of Alice’s acting on the show. Framke commented, “By making her a black woman, Julia tries to be more inclusive than reality without fully anticipating what it would mean for a black woman to produce a show made by wealthy white women and… served who might feel like being more progressive than they really are.”[17] VanArendonk wrote that while Julia shows Alice experiencing harassment in the workplace, she does not force the audience “to think too much about racism. […] Alice is an invention that feels too well invented”.[16]
References[edit]
Julia Computing Raises $24M Series A, Former Snowflake CEO Bob Muglia Joins Board
CAMBRIDGE, Mass., July 19, 2021 – Julia Computing, founded by the developers of the high-performance Julia programming language, today announced the completion of a $24 million Series A fundraising round funded by Dorilton Ventures with participation from Menlo Ventures, General, led by Catalyst and HighSage Ventures. Also announced today is that Bob Muglia, former Snowflake CEO and former Microsoft President of Servers and Tools, will join Julia Computing’s board of directors.
Julia Computing will use the funding to further develop and advance its secure, high-performance cloud platform JuliaHub and grow the Julia ecosystem. JuliaHub makes it easy to develop, deploy, and scale Julia programs and models that data scientists and engineers are rapidly adopting. JuliaHub is not only a standalone cloud computing product, but also a platform for other revolutionary applications, such as AI
Julia Computing was founded by the developers of Julia – one of the fastest and easiest high-performance computing languages for artificial intelligence, machine learning, analytics, data science, modeling and simulation. Julia is used by more than 10,000 companies worldwide including AstraZeneca, BlackRock, Google, Intel, Microsoft, Moderna, Pfizer, as well as NASA, the Federal Aviation Administration and the Federal Reserve Bank of New York.
“The wonders of today’s world are created using digital models. The circuits in our smartphones, advanced materials, pharmaceuticals, and aerospace are all examples of advanced technology created using digital modeling. While these advances have been amazing, the tools and systems supporting these efforts are decades old and fail to take full advantage of the cloud,” said Bob Muglia, member of Julia Computing’s board of directors. “The Julia Computing team shook this world by building JuliaHub, a modern engineering and scientific modeling platform. JuliaHub is poised to advance scientific computing and enable solutions that deliver new generations of products and services that we cannot even imagine today.”
Originally developed at MIT, the Julia programming language has been downloaded more than 29 million times by users worldwide, including thousands of open source developers who have contributed to Julia and its 6,000 registered packages. Over 1,500 universities worldwide use and teach Julia, including world-class institutions such as MIT, Stanford, and UC Berkeley.
Since Julia was introduced to the public in 2012, the community has grown exponentially. Helping scientists and engineers tackle large-scale data science problems, Julia solves the “two-languages problem” by performing the two-step process of testing, modeling, and prototyping in a high-level language (like Python, Matlab, or R) and then rewriting it in a second, faster, lower-level language (like C or C++) for production and scaling. JuliaCon, an annual get-together for the Julia community, takes place from July 28th to 30th and is online this year and free for everyone.
“Technical computing today is stuck in a rut,” said Viral Shah, co-founder and CEO of Julia Computing and co-creator of Julia. “Data scientists and engineers use products that were developed many decades ago. JuliaHub makes it possible to design new medicines and therapies, develop new batteries, simulate a space mission and map the universe, while consuming fewer computing resources and reducing data center emissions. We are truly defining the future of data science and simulation, and it’s exciting to drive these exciting innovations forward. Strengthened by our recent funding, we look forward to growing our team and bringing Julia’s superpowers to more industries and applications.”
Daniel Freeman, who led Dorilton Ventures’ investment, stated, “We are delighted to lead this important round and to work with Julia Computing. Julia Computing is at the heart of technical computing, a significant global market with significant barriers to entry. Julia’s machine learning and AI technologies enable simulation rather than approximation, transforming the economics of computational analysis and scientific discovery. This is a truly transformative business with high potential for success.”
For more information about Julia Computing, visit www.juliacomputing.com.
About Julia Computing
Julia Computing was founded in 2015 by all the developers of the Julia programming language. The company offers scalable enterprise computing solutions through its JuliaHub platform. A new area of focus for the company was the development of a range of advanced ML-enhanced modeling and simulation tools, such as the Pumas framework for pharmaceutical simulation, JuliaSim for multiphysics simulation and JuliaSPICE for circuit simulation. Julia solves the two-language problem by offering high performance and ease of use in a single language. Julia is used by over 10,000 companies and over 1,500 universities. Julia’s creators won the prestigious James H. Wilkinson Prize for Numerical Software and the Sidney Fernbach Award.
About Dorilton Ventures
Dorilton Ventures makes significant minority investments in early to mid-stage technology companies focused on data-centric segments such as IT infrastructure, data science and cybersecurity.
Source: Julia Computing
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