Deep Learning has gained fame and importance like never before by recently owing to its applications in image recognition, voice recognition, pattern detection, etc.
However, do you know which programming language is widely used for deep learning in any domain?
It’s none other than Python.
Python for deep learning, machine learning, and AI has now become one of the biggest trends owing to the versatility and amazing features of the language.
Let’s talk about Python before we get into why it’s chosen for deep learning. Python has now emerged as one of the hottest languages in programming domain. It would not be an exaggeration if Python can be named as the Tony Stark (Iron Man) of the Programming languages.
Reason?
- It’s absolutely easy to learn.
- Python as impeccable libraries.
- It’s an interpreted language.
- Python is built into Linux.
- It is Platform independent & many more!
But why Python is put to use so much in Deep Learning? Before we cover this subject, let us understand;
What is Deep Learning?
Deep learning is the most powerful machine learning technique in existence now! The main idea behind deep learning is that it has been developed based on how a human brain works. Our brain is comprised of millions of neurons working harmoniously. Like that, a deep learning algorithm is a vast number of computation units. It is not that intelligent in isolation yet it has the ability to become intelligent.
To get a better picture, Deep learning is the technology behind Google image search, voice search, etc.
Why Python for Deep Learning?
1. Libraries! Huge in number, and yes, Awesome!
There are a vast number of libraries available in Python. Here are a few of the most effective and commonly used:
- Tensorflow: An open source library specifically used for numerical computation and flow graph. The most astounding feature of Tensorflow is its architecture which enables Python developers and data scientists to deploy computation to one or more CPU or GPU despite the type of device.
- Pytorch: A renowned python package used for its high-level features such as, computation, GPU acceleration & a deep neural network that’s built on a tape-based autograd system.
- Theano: Theano is considered as one of the supreme Python libraries when it comes to deep learning. Theano is basically used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays. Theano can act as the building block of any neural network with ease.
- Keras: Keras use Theanos or TensorFlow as backend. It’s minimalistic and modular in nature and which lets programmers achieve a fast result.
The list of libraries doesn’t end there; there are also other impeccable libraries such as,
- Apache MXnet
- Fast.ai
- CNTK
- TFlearn
- Nolearn
- Elephas
- Spark deep learning
- Sklearn-Theano, etc. and many more!
2. It’s as Simple as It can Get!
Python has no rival when it comes to code readability and ease of use. Data scientists can use Python without too much effort on learning. In short, the learning curve for Python is less owing to its simplicity.
For instance,
est_number = 407 # given example is not a prime number
# prime numbers will always be greater than 1
if test_number > 1:
# check for factors of the numbers
number_list = range(2, test_number)
for number in number_list:
number_of_parts = test_number // number
print(f"{test_number} is not a prime number")
print(f"{number} times {number_of_parts} is {test_number}")
break
else:
print(f"{test_number} is a prime number")
else:
print(f"{test_number} is not a prime number")
Simplicity in coding is evident above. Python’s simple syntax helps in rapid development in comparison to any other language.
3. Immense Online Support!
Many of you know that Python is an open-source programming language. Owing to the same reason it has got a lot of high-quality documents and resources to support. There’s a huge online community present which can help you find out the answer to your queries.
4. Python is so Flexible!
- Python has the option to use it as an OOP or scripting.
- Python can integrate with other languages having no problem at all.
- Results can be quickly seen without recompilation of the source code.
- The imperative, functional, OOP, procedural and functional style makes Python unbeatable.
5. Application of Python in Deep Learning
- Restoring color in black and white photos combining global priors and local image features
- Pixel restoration to enhance distorted pictures
- Real-time analysis of people behavior
- Language translation in real-time using either camera or audio
- Advanced gaming
- Deep learning is helping robotics industry to evolve beyond human imagination
- Self-driving cars
- Mimicking human voice
- Predict the behavior of new airplane routes
- Risk management
There are many more to add! Possibilities and deep learning applications with Python language is endless.
6. Platform? Not a problem for Python
As mentioned above Python is incredibly versatile. The platform is not at all an issue for Python. Be it Windows, Linux, MacOS, Unix, etc. Python can run on all the platforms. All that’s required to be done is some minor alterations and code changes to suit the chosen platform. Voila! Python is ready for action.
Packages like Pyinstaller can also be used for this purpose. Python can save a lot of time and money, thanks to its compatibility features.
7. Visualization
In this blog, various libraries and framework of Python have already been mentioned. Apart from that, Python also has libraries which are impeccable when it comes to data visualization. To represent data for humans such visualization is necessary when it comes to AI, deep learning or machine learning.
Libraries of Python in such functionality include; matplotlib, Seaborn, ggplot, Bokeh, pygal, Plotly, geoplotlib, Gleam, missingno, etc.
Summary
We are at a juncture of time where AI, deep learning and machine learning has a profound influence on our life. Owing to the same, the pace should increase in this area when it comes to development. No other Languages can help in this rather than Python. Its myriad of features, easy to read and write features and extensive libraries makes it the best tool possible for the purpose.
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