1. Learn TensorFlow.js - Deep Learning and Neural Networks with JavaScript (by @deeplizard and freecodecamp)
2. Neural Networks with JavaScript - Full Course using Brain.js (by @robertlplummer and freecodecamp)
Coding
└── Machine learning frameworks
1. TensorFlow 2.0 Complete Course (by @TechWithTimm and freecodecamp)
2. Keras with TensorFlow Course - Python Deep Learning and Neural Networks for Beginners (by @deeplizard and freecodecamp)
3. PyTorch for Deep Learning (by @jovianhq and freecodecamp)
4. Scikit-learn Crash Course - Machine Learning Library for Python (by @fishnets88 and freecodecamp)
If you are still here, and perhaps finished some courses after coming back to this list, congratulations! You are off to a great start in machine learning.
Now go, and build something awesome!
We are pushing the limits of Twitter, as I cannot add any more tweets to this thread :)
I post threads like this every week, diving deep into concepts in machine learning and mathematics.
If you have enjoyed this, make sure to follow me and stay tuned for more!
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Data similarity has such a simple visual interpretation that it will light all the bulbs in your head.
The mathematical magic tells you that similarity is given by the inner product. Have you thought about why?
This is how elementary geometry explains it all.
↓ A thread. ↓
Let's start in the beginning!
In machine learning, data is represented by vectors. So, instead of observations and features, we talk about tuples of (real) numbers.
Vectors have two special functions defined on them: their norms and inner products. Norms simply describe their magnitude, while inner products describe
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well, a 𝐥𝐨𝐭 of things.
If I toss a fair coin ten times and it all comes up heads, what is the chance that the 11th toss will also be heads? Many think that it'll be highly unlikely. However, this is incorrect.
Here is why!
↓ A thread. ↓
In probability theory and statistics, we often study events in the context of other events.
This is captured by conditional probabilities, answering a simple question: "what is the probability of A if we know that B has occurred?".
Without any additional information, the probability that eleven coin tosses result in eleven heads in a row is extremely small.
However, notice that it was not our case. The original question was to find the probability of the 11th toss, given the result of the previous ten.
The early access of my Mathematics of Machine Learning book is launching today!
One chapter per week, we go from basics to the internals of neural networks. We are starting with vector spaces, the scene where machine learning happens.
Here is why they are so important!
🧵 👇🏽
As you probably know, data is represented by vectors.
Data points are just tuples of measurements. In their raw form, they are hardly useful for us. They are just blips in space.
Without operations and transformations, it is difficult to predict class labels or do anything else.
Vector spaces provide a mathematical structure where operations naturally arise.
Instead of a blip, just imagine an arrow pointing to the data point from a fixed origin.
Even though most of us are introduced to the subject through this example, fitting functions to a training dataset seemingly doesn't give us any deep insight about the data.
This is what's working behind the scenes!
🧵 👇🏽
Consider a simple example: predicting the value 𝑦 from the observation 𝑥; for instance 𝑦-s are real estate prices based on the square footage 𝑥.
If you are a visual person, this is how you can imagine such dataset.
The first thing one would do is to fit a linear function 𝑓(𝑥) = 𝑎𝑥 + 𝑏 on the data.
By looking at the result, we can see that something is not right. Sure, it might capture the mean value for a given observation, but the variance and the noise in the data is not explained.