**What is a Python Library?**

A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Consider it as a collection of functions and methods that allows you to perform many actions without writing your own code.

For example, if you were baking a cake and needed flour, you wouldn’t go through the process of grinding wheat to make flour.

**Instead, you would go to the store and buy a packet of flour, which is ready to use. RIght?**

Similarly, Python libraries are like those packets of flour, pre-made and ready to use, saving you a lot of time and effort in coding from scratch.

**What is Deep Learning?**

**Deep Learning is a subset of machine learning, which is essentially a neural network with three or more layers. **These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—to ‘learn’ from large amounts of data.

While a neural network with a single layer can still make approximate predictions, additional hidden layers can help optimize the accuracy. Imagine trying to catch fish in a large pond.

**Neural Network**: A series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

You can randomly throw your net and hope to catch something. But if you understood where fish like to gather or what time of the day they come near the surface, you could optimize your catch.

The extra layers in deep learning help in the extraction of such features and improve the prediction or classification ability of the model.

See Also: What is Deep Learning? Types, Pros&Cons | Easy Definition

**What is Theano?**

**Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions, especially those involving multi-dimensional arrays.**

It’s particularly useful for manipulating and analyzing mathematical expressions, especially those involving matrices. Theano is designed to handle the types of computation required for large neural network algorithms used in Deep Learning.

It was developed by the Université de Montréal and has been available since 2007.

**Features of Theano**

Theano is particularly well-suited for deep learning for a few reasons.

**Multi-dimensional Arrays**

Firstly, it allows for efficient computation with multi-dimensional arrays, which are often used in deep learning algorithms.

**Multi-dimensional arrays**: In Python, these are data structures that can store data in more than one dimension. They are often used in mathematical computations.

**Efficient Calculation**

Secondly, Theano is able to take advantage of both CPU and GPU processing power, which can significantly speed up the computations required for training deep learning models.

**CPU**: Central Processing Unit. This is the primary component of a computer that performs most of the processing inside the computer.**GPU**: Graphics Processing Unit. This is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. It’s used in deep learning to speed up computations.

**Flexibility**

Lastly, Theano provides a lot of flexibility and control, which can be very useful when implementing complex deep learning models.

**Differences between Theano, Keras, and Scikit-Learn**

Theano, Keras, and Scikit-Learn are all powerful tools in the field of machine learning and data science, but they each have their own strengths and uses.

Library | Key Features |
---|---|

Theano | – Python library for defining, optimizing, and evaluating mathematical expressions – Handles multi-dimensional arrays – Useful for manipulating and analyzing mathematical expressions – Designed for computations required for large neural network algorithms in Deep Learning |

Keras | – High-level neural networks API – Runs on top of TensorFlow, CNTK, or Theano – Focuses on enabling fast experimentation – User-friendly, modular, and extensible – Great tool for beginners in deep learning |

Scikit-Learn | – Free software machine learning library for Python – Features various classification, regression and clustering algorithms – Interoperates with Python numerical and scientific libraries NumPy and SciPy – Suited for traditional machine learning tasks that don’t involve neural networks |

**How to Install Theano**

Theano can be installed on various operating systems including Windows, MacOS, and Linux.

**I’ll explain how to install Theano on each operating systems.**

**Windows & Linux**

**1. Install Dependencies**

**Theano has a few dependencies that need to be installed first.**

These include Python, NumPy, SciPy, and pydot. You can install these using pip, a package manager for Python.

Open your command prompt / terminal and type the following commands:

```
$ pip install numpy
$ pip install scipy
$ pip install pydot
```

**2. Install Theano**

Once the dependencies are installed, you can install Theano. In your command prompt, type:

`$ pip install Theano`

**macOS**

**1. Install Dependencies**

**Theano has a few dependencies that need to be installed first. These include Python, NumPy, SciPy, and pydot.**

You can install these using pip, a package manager for Python. Open your terminal and type the following commands:

```
$ pip install numpy
$ pip install scipy
$ pip install pydot
```

**2. Install Theano**

Once the dependencies are installed, you can install Theano. In your terminal, type:

`$ pip install Theano`

**3. Install OSx Command Line Developer Tool**

This tool is necessary for some of Theano’s features. Install it using the following command:

`$ xcode-select --install`

**Installation Check**

After the installation is complete, you can test if Theano was installed correctly.

Open a new Python script or Jupyter notebook and type the following:

```
import theano from theano
import tensor
a = tensor.dscalar()
b = tensor.dscalar()
c = a + b
f = theano.function([a,b], c)
d = f(1.5, 2.5)
print (d)
```

If the output is ” 4.0 ” then Theano was installed successfully.

**5 Best Free Tutorials of Theano for Beginners**

**1. Machine Learning Mastery**

Theano Tutorial by Machine Learning Mastery

This tutorial is a great starting point for beginners. It provides a comprehensive introduction to Theano, including how to install it and how to start building simple neural networks.

**2. Project Pro**

Theano Tutorial by Project Pro

This tutorial provides a more in-depth look at Theano, including how to use it to build more complex deep learning models.

**3. DataCamp**

This tutorial is particularly good for those who prefer learning through interactive, hands-on exercises.

**4. WildML**

This tutorial is a bit more advanced, but it’s great for those who want to learn how to use Theano to speed up their neural network computations using a GPU.

**5. Analytics Vidhya**

Theano Tutorial by Analytics Vidhya

This tutorial is comprehensive and well-structured, making it a good option for those who prefer a more systematic approach to learning.