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TF Ops (Part 2)

Explore common TensorFlow operations for manipulating tensors such as transpose, matrix multiplication, reshape, and reduction functions. Understand key linear algebra operations like eigendecomposition, matrix inverse, and least squares solutions to apply in deep learning models effectively.

We'll cover the following...

Common TF ops

Here, we discuss some important TF ops applied to tensors. These include the following:

  • tf.transpose(): This is for taking the transpose of a tensor.

  • tf.matmul(): This is for matrix (tensor) multiplication.

  • tf.reshape(): This is to change the shape of the tensor.

  • tf.reduce_mean(): This is for reducing a tensor using its mean (equivalent to np.mean() in NumPy).

  • tf.reduce_sum(): This is for reducing a tensor using its sum (equivalent to np.sum() in NumPy).

  • tf.reduce_max(): This is for reducing a tensor using its maximum value (equivalent to np.max() in NumPy). ...