Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow
Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.
TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
You see? The name “TensorFlow” is derived from the operations which neural networks perform on multidimensional data arrays or tensors! It’s literally a flow of tensors. For now, this is all you need to know about tensors, but you’ll go deeper into this in the next sections!
Today’s TensorFlow tutorial for beginners will introduce you to performing deep learning in an interactive way:
You’ll first learn more about tensors;
Then, the tutorial you’ll briefly go over some of the ways that you can install TensorFlow on your system so that you’re able to get started and load data in your workspace;
After this, you’ll go over some of the TensorFlow basics: you’ll see how you can easily get started with simple computations.
After this, you get started on the real work: you’ll load in data on Belgian traffic signs and exploring it with simple statistics and plotting.
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