All posts in: SoftMax

Before reading this article, I recommend to have a look at my previous article on TensorFlowKit and like my repository.

When I started working in the field of machine learning, it was quite difficult to move to vectors and spaces from objects and their behavior. At first it was rather complicated to wrap my head around all that, and most processes did not seem obvious and clear at once. That’s the reason why I did my best to visualize everything I did in my groundwork:  I used to create 3D models, graphs, diagrams, figures, etc.

When speaking about efficient development of machine learning systems, usually such problems as learning speed control, learning process analysis, gathering various learning metrics, and others are mentioned. The major difficulty is that we (people) use 2D and 3D spaces to describe various processes that take place around us. However, processes within neural networks lay in multidimensional spaces, and that makes them rather difficult to understand. Engineers all around the world understand this problem and try to develop various approaches to the visualization or conversion of multidimensional data into simpler and more understandable forms.

There are separate communities dedicated to solving such problems, for example, Distill, Welch Labs, 3Blue1Brown.

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Please, read my previous post about Swift & TensorFlow

MNIST

I took “Hello World!”  in the universe of neural networks as an example, a task for systematization of MNIST images. MNIST dataset includes thousands of images of handwritten numbers, the size of each image is 28×28 pixels. So, we have ten classes that are neatly divided into 60 000 images for educating and 10 000 images for testing. Our task is to create a neural network that is able to classify an image and determine the class it belongs to (out of 10 classes).

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