Technology has been an ever-evolving field since the beginning of its evolution, and every innovation or invention aims to ease or aid human effort in a specific domain, either directly or indirectly. The invention of computers accelerated technological evolution exponentially, and owing to that and the internet, we are the most connected people in the history of humanity.
Now, technology has taken an even bigger leap in evolution and growth due to breakthroughs in a domain that is called artificial intelligence. This has many uses in today’s world and has significantly reduced human effort. All of it is possible because of deep learning with python.
Artificial intelligence, with machine learning, aims to build computers and systems that perform calculations, devise algorithms and even take certain decisions on their own, thereby reducing human effort further.
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What is Machine Learning?
Machine learning is a subset of artificial intelligence that employs different algorithms and methods to recognize previous data patterns and predict the next step for the machine to work upon or to solve some pre-fed problem or data. This has reduced human intervention as a decision-maker and allowed the machines to work on their own on any issue or problem, which has increased the efficiency and accuracy of the system where artificial intelligence is employed. Through artificial intelligence, instead of using several people to work and process a certain amount of data, a single data science professional can be employed to train a neural network and employ that network to do the work of processing data on its own.
It is a very widely used method for mathematical modeling, processing big data, etc.
Introduction to Deep Learning
Deep learning is a subset of a broader domain i.e., machine learning. When we talk about machine learning as a means to make programs self-decisive, we refer to this portion of machine learning as it is the part that details supervised, semi-supervised or unsupervised processing of data by an artificial intelligence network or a system.
Deep-learning uses layers of several networks called the neural networks that are trained by data science personnel and stacked upon each other to perform a specified operation. This stacking of neural networks is why we term this process ‘deep’ learning.
Why Use Python for Deep Learning?
Python is a very useful programming language when it comes to artificial intelligence or machine learning as a general subject. But its usefulness increases when we talk about deep learning in particular because apart from being a ‘concise syntax’ language, it also provides several libraries such as TensorFlow and Theano, specifically for training and building neural networks, stacking them together, and other necessary features.
Also Read: Why Machine Learning with Python?
Python offers concise and readable code. Python code is understandable by way of human beings, which makes it easier to build models for system mastering.
Here are a few libraries that help in solving various aspects of deep learning:
- SciPy and Seaborn for advanced computing and data visualization.
- Pandas and NumPy for general and high-performance data analysis
Apart from providing dedicated libraries for training neural networks and having concise syntax, there are several other reasons as to why deep learning with python is the preferred approach by leading data science professionals.
Here are some of those reasons:
- platform-independent
- vast community: helps in forming a social network to resolve any problem that may arise in building a neural network system. Problem-solving becomes easier and faster.
Other languages for AI programming
We now have studied why deep learning with python is a preferred approach for new as well as experienced data scientists but knowing new and other platforms for developing AI programs and systems is also very important for a skilled data scientist or enthusiast.
Programming languages like R, Java, Julia are also employed in developing AI programs; developers are also employing the above-mentioned languages to develop compatible libraries to widen the scope of these languages in the field of AI and deep learning.
Platforms like R and Julia are also very useful for developing AI programs and training neural networks as they also provide some similar libraries as python. Experienced data scientists/engineers and other professionals advise not to just stick to python but to explore and expand one’s skill set when it comes to deep learning and artificial intelligence in general.
Conclusion
Artificial intelligence is termed as the future of technology and thus provides very vast and promising career and innovation opportunities for budding enthusiasts. Deep learning and machine learning require robust knowledge and sharp programming skills to innovate and create AI programs that help us reduce manpower and increase efficiency.
Python provides an extensive list of tools to learn and implement machine learning and layered neural networks for the same.
