Whether it be to deploy deep learning models or to tackle machine learning problems at the edge, many people are finding success with the Raspberry Pi platform. If you're looking to set up a Raspberry Pi on your own and get started with machine learning, then this post is for you.
First and foremost, you'll need to set up your Raspberry Pi to serve as an IoT platform. There are many guides available for setting up a Raspberry Pi for IoT use. We suggest going over this one from Adafruit to familiarize yourself with the basics.
Once your Raspberry Pi is setup, you'll need to configure your machine learning environment. One option is to install the Anaconda Distribution of Python. Once installed, you can use the Anaconda Navigator to create a virtual environment with the TensorFlow libraries.
Once you have your machine learning environment set up, you can start playing around with the various open source deep learning frameworks. The two most popular are Keras and TensorFlow. Both have easy to use APIs and are relatively straightforward to set up and get started with on your Raspberry Pi.
We suggest starting with the Keras tutorials to get a feel for the platform. Once you have a hang of the fundamentals, you can start experimenting with basic projects to better understand deep learning and AI. To get a deeper understanding, you can dive into the TensorFlow tutorials for that.
Finally, once you have the basics nailed down, don't forget to explore the wider world of ML and its many applications.There are many tutorials and repositories available online to get you started, such as this very useful repository of projects and tutorials.
Good luck and have fun tinkering!
import tensorflow as tf # code to create a basic TensorFlow model model = tf.keras.models.Sequential() model.add(tf.keras.layers.Dense(units=64, activation='relu', input_dim=32)) model.add(tf.keras.layers.Dense(units=32, activation='relu')) model.add(tf.keras.layers.Dense(units=1, activation='sigmoid')) model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['accuracy'])