Why I Never Write Long Functions

I only write small functions. You may think this means I have a hard line limit on my functions, but actually I think that is a terrible idea. Instead I make my functions so they can be read even by someone with no programming experience.

⏱️ Timestamps:

  • 00:00 - Introduction
  • 01:10 - What Not To Do
  • 02:25 - How To Use Small Functions
  • 03:39 - Small Function Benefits
  • 05:50 - How To Refactor To Small Functions
  • 08:19 - Gotchas


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Why I Never Write Long Functions
Vincent Lab

Vincent Lab


The Difference Between Regular Functions and Arrow Functions in JavaScript

Other then the syntactical differences. The main difference is the way the this keyword behaves? In an arrow function, the this keyword remains the same throughout the life-cycle of the function and is always bound to the value of this in the closest non-arrow parent function. Arrow functions can never be constructor functions so they can never be invoked with the new keyword. And they can never have duplicate named parameters like a regular function not using strict mode.

Here are a few code examples to show you some of the differences
this.name = "Bob";

const person = {
name: “Jon”,

<span style="color: #008000">// Regular function</span>
func1: <span style="color: #0000ff">function</span> () {
    console.log(<span style="color: #0000ff">this</span>);

<span style="color: #008000">// Arrow function</span>
func2: () =&gt; {
    console.log(<span style="color: #0000ff">this</span>);


person.func1(); // Call the Regular function
// Output: {name:“Jon”, func1:[Function: func1], func2:[Function: func2]}

person.func2(); // Call the Arrow function
// Output: {name:“Bob”}

The new keyword with an arrow function
const person = (name) => console.log("Your name is " + name);
const bob = new person("Bob");
// Uncaught TypeError: person is not a constructor

If you want to see a visual presentation on the differences, then you can see the video below:

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What You Can Learn about Setting from Classic Sitcoms

Giving your novel a strong sense of place is vital to doing your part to engage the readers without confusing or frustrating them. Setting is a big part of this (though not the whole enchilada — there is also social context and historic period), and I often find writing students and consulting clients erring on one of two extremes.

**Either: **Every scene is set in a different, elaborately-described place from the last. This leads to confusion (and possibly exhaustion and impatience) for the reader, because they have no sense of what they need to actually pay attention to for later and what’s just…there. Are the details of that forest in chapter 2 important? Will I ever be back in this castle again? Is there a reason for this character to be in this particular room versus the one she was in the last time I saw her? Who knows!

Or: There are few or no clues at all as to where the characters are in a scene. What’s in the room? Are they even in a room? Are there other people in th — ope, yes, there are, someone just materialized, what is happening? This all leads to the dreaded “brains in jars” syndrome. That is, characters are only their thoughts and words, with no grounding in the space-time continuum. No one seems to be in a place, in a body, at a time of day.

Everything aspect of writing a novel comes with its difficulties, and there are a lot of moving pieces to manage and deploy in the right balance. When you’re a newer writer, especially, there’s something to be said for keeping things simple until you have a handle on how to manage the arc and scope of a novel-length work. And whether you tend to overdo settings or underdo them, you can learn something from TV, especially classic sitcoms.

Your basic “live studio audience” sitcoms are performed and filmed on sets built inside studios vs. on location. This helps keep production expenses in check and helps the viewer feel at home — there’s a reliable and familiar container to hold the story of any given episode. The writers on the show don’t have to reinvent the wheel with every script.

Often, a show will have no more than two or three basic sets that are used episode to episode, and then a few other easily-understood sets (characters’ workplaces, restaurants, streets scenes) are also used regularly but not every episode.

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Hal  Sauer

Hal Sauer


Learn to Write Better Code in Our Python Advanced Functions Course

Writing better code isn’t always front-of-mind when it comes to learning data science — we’re often more concerned with just getting the code to work, and making sure the analysis is sound.

But to work as part of an effective data science team, you’ve got to be able to write code that’s readable, maintainable, testable, and debuggable, not just code that’s functional.

That’s why we’re happy to announce we’ve just launched a new course in our Python Data Scientist path called Functions: Advanced.

It’s an in-depth Python functions course that’s designed to show you how to write better code using functional programming. If you’re working in data — or aspire to work in data — this course covers critical skills for making your code easier to read, maintain, test, and debug.

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Tia  Gottlieb

Tia Gottlieb


Activation Functions, Optimization Techniques, and Loss Functions

Activation Functions:

A significant piece of a neural system Activation function is numerical conditions that decide the yield of a neural system. The capacity is joined to every neuron in the system and decides if it ought to be initiated (“fired”) or not, founded on whether every neuron’s info is applicable for the model’s expectation. Initiation works likewise help standardize the yield of every neuron to a range somewhere in the range of 1 and 0 or between — 1 and 1.

Progressively, neural systems use linear and non-linear activation functions, which can enable the system to learn complex information, figure and adapt practically any capacity speaking to an inquiry, and give precise forecasts.

Linear Activation Functions:

**Step-Up: **Activation functions are dynamic units of neural systems. They figure the net yield of a neural node. In this, Heaviside step work is one of the most widely recognized initiation work in neural systems. The capacity produces paired yield. That is the motivation behind why it is additionally called paired advanced capacity.

The capacity produces 1 (or valid) when info passes edge limit though it produces 0 (or bogus) when information doesn’t pass edge. That is the reason, they are extremely valuable for paired order studies. Every rationale capacity can be actualized by neural systems. In this way, step work is usually utilized in crude neural systems without concealed layer or generally referred to name as single-layer perceptions.

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Aria Smith


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