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Unlike machine learning, deep learning requires lesser human supervision. Thus the hardware requirement is way more extensive in the case of deep learning. Deep learning models are relatively automated and multiple layers of processing are essential. The training process is less tedious but significantly long due to a lesser degree of human intervention. The training is longer than usual, due to the importance of long duration optimization.

A closer look at the structural and functional aspects of deep learning reveals the reliance of deep learning models on neural networks. Multiple layers of artificial neurons are programmed to utilize most of the data and obtain results almost automatically. With a greater degree of independence over machine learning models.

Clearly, to develop something as complicated as a deep learning model significant coding effort is important. And the effort becomes significantly lesser with an easier and more comprehensible language. Python is perhaps the best candidate for such endeavors and this article will try to explore if python really fits in as the perfect choice. 

 

The nature of python

Deep learning with python has always been a beginner’s choice. The language is easy to understand and remember. The syntax of python is relatively closer to natural language and writing codes is less difficult. Similar codes can be written with significantly less effort with python compared to JAVA and R. Fewer number lines can satisfy the need in the case of python hence, reduces the risk of errors. 

Due to the easygoing nature of the language, debugging and troubleshooting is relatively easy. Locating a bug takes significantly less time due to its simple and crisp nature of syntax. 

The ease and smoothness come at a cost. Python is a slow language when it comes to performance. The language is powerful but requires less pronounced hardware support. 

 

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Libraries 

Libraries consist of pre-designed codes for performing usual and routine tasks. By accessing these libraries a programmer can take out necessary code segments and use them instead of typing them by themself. Tensorflow, Keras, and PyTorch are the most used libraries in the case of deep learning model development. Keras is significantly easy and offers limited functionality but an easy interface. TensorFlow and Pytorch are widely favored, being used in industries and for research purposes extensively. Libraries of python offer a wide range and are extremely easy to utilize. And due to the availability and variety of these libraries, there is always some help while designing a model. Libraries offer a lot of prewritten codes effectively reducing the complexity of deep learning model development. 

 

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Community 

Python is around for a while. After its inception in the ’80s, the easy nature of the language impressed many. Python was receiving wide acceptance from academic and industrial fields fueling its development. The first generation of python users are the most experienced class of coders today and surprisingly they are mostly still professionals. The presence of this community inspires more and more coders to make a switch to python. 

Due to this benevolent presence of a large number of senior coders, troubleshooting is not even an issue. The questions and queries placed in forums and community-specific groups never remain unanswered. This friendly environment is poised to increase the number of users in an exponential manner over the years. Which in turn will benefit the new users in terms of support and knowledge.

 

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Author’s note

After the pandemic businesses and public sectors are gradually leaning towards data. Analytics has become a vital component of strategy venture ideas. Deep learning tools are witnessing an era of massive implementation in the cases of commerce and public services. Due to the availability of deep learning tools, it is possible today to predict the market and natural phenomena way more effectively. Natural disasters and economical inconveniences are predicted way before the incident today and dealt with ease because of deep learning. This wide applicability has given rise to recruitment opportunities.

IT and computer science professionals are suffering today in sync with the sufferings of economic and industrial giants. On a global scale, this demise of IT and the rise of automation is complementing each other effectively. Perhaps the time is absolutely right for training in deep learning with python for a sustained future. Due to the experience, existing python users will have the upper hand in the case of deep learning. But the easy nature of the Python language will eventually ease up the difficulties and provide a fair fighting chance for the newbies as well.