React is a powerful library which enables to build complex and scalable user
interfaces for the web and mobile. This article is for react developrs who want
improve their skills and dive a little deeper into the react ecosystem. If you
have build a small or medium sized application this article is right for you.
Python is an Object Oriented Language (OOP) which can be used to write
procedural code as well. The procedural programming style consists of splitting
a program into several functions (procedures or subroutines). Data goes into the
function and the function returns results ideally without mutating the data it
received.
Algorithms and data structures are much more than abstract concepts. Mastering
them enables you to write code that runs faster and more efficiently, which is
particularly important for today’s web and mobile apps. We will cover data
structures and algorithms which can be used daily in production code.
k-Nearest Neighbors is a classification algorithm. Assuming we want to decide if
a fruit is an orange or a grapefruit, we can create a graph ploting different
fruits based on their size.
Dynamic programming is a an interesting way of solving problems using recursion.
At it’s core, it consists of breaking down a problem into simpler to solve
sub problems recursively. A famous problem to show case this is the backpack
problem. The backpack problem is a very famous computer science problem.
Assuming we have a backpack that can hold 4kg worth of items we want to figure
out how many items we can fit while at the same time archiving the highest
amount of value possible.
Blockchain is one of the lastest buzz words in the tech community. Businesses
seem to love it and a whole new branch of blockchain developers has emerged.
However, what is the blockchain and how does it work ?
A key part of deep learning is to evaluate how good the prediction is. We do
this by comparing. The error output will tell us if we missed a lot or a little.
Once we have a way to measure the error we can move on to the next step, learn.
A greedy algorithm is simple in each step and that is it’s beauty. In each
each step, you pick the optimal move. Suppose we want to fill a classroom for
as much as possible with classes.
Now we will look closer at prediction. A neural network requires a lot of data
to accurately predict. Similar to how a human would learn. We can build a neural
network that can predict based on a single input. It takes the input and adjust
its parameter or weight to produce an output (prediction).
