- 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 :)
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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)
🧵👇
- 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)
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🔗//colab.research.google.com/drive/1FxSrY6hwdgszzNydyobTLsMyO0bfpiPs?usp=sharing
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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?👇
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In these neural networks, data flows through them and each neuron (the circle) has a numerical value which will change.
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Think of the neurons as dials on a lock, you have to tune every dial to open the lock.
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Once the dials are well tuned, you have a well trained neural network!
...
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3. Now we pass the data (flats and prices) through our neural network 500 times. (these loops are called epochs)
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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 👇
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More from Pratham Prasoon
More from Machine learning
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
