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README.md

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 <img src="https://explained.ai/tensor-sensor/images/teaser.png" width="50%" align="right">One of the biggest challenges when writing code to implement deep learning networks, particularly for us newbies, is getting all of the tensor (matrix and vector) dimensions to line up properly. It's really easy to lose track of tensor dimensionality in complicated expressions involving multiple tensors and tensor operations.  Even when just feeding data into predefined [Tensorflow](https://www.tensorflow.org/) network layers, we still need to get the dimensions right. When you ask for improper computations, you're going to run into some less than helpful exception messages.  To help myself and other programmers debug tensor code, I built this library.  TensorSensor clarifies exceptions by augmenting messages and visualizing Python code to indicate the shape of tensor variables (see figure to the right for a teaser). It works with [Tensorflow](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), and [Numpy](https://numpy.org/), as well as higher-level libraries like [Keras](https://keras.io/) and [fastai](https://www.fast.ai/).
 
+Please read the complete description in article [Clarifying exceptions and visualizing tensor operations in deep learning code](https://explained.ai/tensor-sensor/index.html).
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 *TensorSensor is currently at 0.1b1 so I'm happy to receive issues created at this repo or direct email*.
 
 ## Visualizations