TensorFlow Hub has models for all the ML domains such as Image, Text, Audio and Video
For image, this tutorial can get you started with Transfer Learning. It does some cool tricks with the data and the final model is ready for on-device deployment
Audio is a very interesting domain
We usually think about understanding speech as the main task but it can do much more like audio classification and pitch detection
Machine learning goes beyond Deep Learning and Neural Networks
Sometimes a simpler technique might give you better results and be easier to understand
A very versatile algorithm is the Decision Forest
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What is it and how does it work?
Let me tell you..
[7 min]
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Before understanding a Forest, let's start by what's a Tree
Imagine you have a table of data of characteristics of Felines. With features like size, weight, color, habitat and a column with the labels like lion, tiger, house cat, lynx and so on.
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With some time, you could write a code based on if/else statements that could, for each a row in the table, decide which feline it is
This is exactly what a Decision Tree does
During its training it creates the if/elses
Sometimes you need to build a Machine Learning model that cannot be expressed with the Sequential API
For these moments, when you need a more complex model, with multiple inputs and outputs or with residual connections, that's when you need the Functional API!
[2.46 min]
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The Functional API is more flexible than the Sequential API.
The easiest way to understand is to visualize the same model created using the Sequential and Functional API
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You can think of the Functional API as a way to create a Directed Acyclic Graph (DAG) of layers while the Sequential API can only create a stack of layers.
Functional is also known as Symbolic or Declarative API