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#### Pros

### Pro Optimized for both CPU and GPU

Since all variables are actually symbolic variables, you need to define a function and fill in the values in order to get a value. For example:

```
# X, y and w are a matrix and vectors respectively
# E is a scalar that depends on the above variables
# to get the value of E we must define:
Efun = theano.function([X,w,y],
E,allow_input_downcast=True)
```

While this seems like an unnecessary step, it's actually not. Since Theano now has a representation of the whole expression graph for the Efun function, it can compile and optimize the code so that it can run on both CPU and GPU.

### Pro Well adapted for numerical tasks

Theano is a Python library which is very well adapted for numerical tasks often encountered when dealing with deep learning.

What makes it well adapted for those tasks is the fact that it combines several paradigms for numerical computations, namely:

matrix operations

symbolic variable and function definitions

Just-in-time compilation to CPU or GPU machine code

#### Cons

### Con Somewhat low level on its own

Theano is one of the oldest deep learning libraries out there and a lot of other widely used libraries have been built on top of it.

But Theano heavily relies on the mathematical side of deep learning and data discovery, having similar features to NumPy or Matlab. This is why it's usually used with other libraries in order to achieve a higher level of abstraction.