A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
🧵👇

For this exercise, you'll need :
- A computer/phone
- An internet connection
- Very basic Python knowledge
- Willingness to learn

What you do not need:
- Complex math
- An expensive computer
- A PhD

Math will come later on in your machine learning journey :)

(2 / 15)
Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

(3 / 15)
We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

(4 / 15)
Here's the code👇

(5 / 15)
In order to avoid the hassle of the setup for python, we'll use collab which has all the libraries we need pre installed, a free GPU for faster learning and everything runs in the cloud!

🔗//colab.research.google.com/drive/1FxSrY6hwdgszzNydyobTLsMyO0bfpiPs?usp=sharing

(6 / 15)
Let's try to understand what is going on here

1. We import TensorFlow and Keras which are frameworks for making neural nets

2. Our Neural Net: This is where all the magic happens, for this exercise we need only one neuron.

Wait! What is a neural net?👇

(7 / 15)
Neural Networks are a digital imitation of the neurons you see in the human brain.

In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.

(8 / 15)
The value of a neuron gets changes to something which is close to what we want each time the data passes through the neural network.

Think of the neurons as dials on a lock, you have to tune every dial to open the lock.

(9 / 15)
It is almost impossible for a human to tune thousands of dials like these, but a computer certainly can.

Once the dials are well tuned, you have a well trained neural network!
...

(10 / 15)
... In this case we'll be able to predict the prices of houses based on how many flats they have.

3. Now we pass the data (flats and prices) through our neural network 500 times. (these loops are called epochs)

(11 / 15)
4. Finally,we predict what the price of a house with 10 flats. (we should get something around 100,000)

And that's it! It was that easy.
In case you want to improve your python and machine learning skills after this exercise, these are great frfee courses to take 👇

(12 / 15)
Machine learning foundations course
🔗//youtube.com/watch?v=_Z9TRANg4c0

> A simple yet extremely effective course on getting started with machine learning without all the crazy math.

(13 / 15)
The Basic & Intermediate Python course on freecodecamp
🔗Basics //youtube.com/watch?v=rfscVS0vtbw
🔗Intermediate //youtube.com/watch?v=HGOBQPFzWKo

(14 / 15)

More from Pratham Prasoon

More from Machine learning

Happy 2⃣0⃣2⃣1⃣ to all.🎇

For any Learning machines out there, here are a list of my fav online investing resources. Feel free to add yours.

Let's dive in.
⬇️⬇️⬇️

Investing Services

✔️ @themotleyfool - @TMFStockAdvisor & @TMFRuleBreakers services

✔️ @7investing

✔️ @investing_city
https://t.co/9aUK1Tclw4

✔️ @MorningstarInc Premium

✔️ @SeekingAlpha Marketplaces (Check your area of interest, Free trials, Quality, track record...)

General Finance/Investing

✔️ @morganhousel
https://t.co/f1joTRaG55

✔️ @dollarsanddata
https://t.co/Mj1owkzRc8

✔️ @awealthofcs
https://t.co/y81KHfh8cn

✔️ @iancassel
https://t.co/KEMTBHa8Qk

✔️ @InvestorAmnesia
https://t.co/zFL3H2dk6s

✔️

Tech focused

✔️ @stratechery
https://t.co/VsNwRStY9C

✔️ @bgurley
https://t.co/NKXGtaB6HQ

✔️ @CBinsights
https://t.co/H77hNp2X5R

✔️ @benedictevans
https://t.co/nyOlasCY1o

✔️

Tech Deep dives

✔️ @StackInvesting
https://t.co/WQ1yBYzT2m

✔️ @hhhypergrowth
https://t.co/kcLKITRLz1

✔️ @Beth_Kindig
https://t.co/CjhLRdP7Rh

✔️ @SeifelCapital
https://t.co/CXXG5PY0xX

✔️ @borrowed_ideas

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Хајде да направимо мали осврт на случај Мика Алексић .

Алексић је жртва енглеске освете преко Оливере Иванчић .
Мика је одбио да снима филм о блаћењу Срба и мењању историје Срба , иза целокупног пројекта стоји дипломатски кор Британаца у Београду и Оливера Иванчић


Оливера Илинчић је иначе мајка једне од његових ученица .
Која је претила да ће се осветити .

Мика се налази у притвору због наводних оптужби глумице Милене Радуловић да ју је наводно силовао човек од 70 година , са три бајпаса и извађеном простатом пре пет година

Иста персона је и обезбедила финансије за филм преко Беча а филм је требао да се бави животом Десанке Максимовић .
А сетите се и ко је иницирао да се Десанка Максимовић избаци из уџбеника и школства у Србији .

И тако уместо романсиране верзије Десанке Максимовић утицај Британаца

У Србији стави на пиједестал и да се Британци у Србији позитивно афирмишу како би се на тај начин усмерила будућност али и мењао ток историје .
Зато Мика са гнушањем и поносно одбија да снима такав филм тада и почиње хајка и претње која потиче из британских дипломатских кругова

Најгоре од свега што је то Мика Алексић изговорио у присуству високих дипломатских представника , а одговор је био да се све неће на томе завршити и да ће га то скупо коштати .
Нашта им је Мика рекао да је он свој живот проживео и да могу да му раде шта хоће и силно их извређао
Tip from the Monkey
Pangolins, September 2019 and PLA are the key to this mystery
Stay Tuned!


1. Yang


2. A jacobin capuchin dangling a flagellin pangolin on a javelin while playing a mandolin and strangling a mannequin on a paladin's palanquin, said Saladin
More to come tomorrow!


3. Yigang Tong
https://t.co/CYtqYorhzH
Archived: https://t.co/ncz5ruwE2W


4. YT Interview
Some bats & pangolins carry viruses related with SARS-CoV-2, found in SE Asia and in Yunnan, & the pangolins carrying SARS-CoV-2 related viruses were smuggled from SE Asia, so there is a possibility that SARS-CoV-2 were coming from