Over the years, humans have become more and more dependent on machines. The present era is the era of technological revolution where AI (artificial intelligence) is at the forefront. Whether in touch-sensitive machine control or human-like robots, artificial intelligence is spreading its roots deeper and deeper.
Technological innovation paved new opportunities for projects and solutions that support decentralization, digitalization, and related concepts. Early people related Artificial Intelligence to sci-fi movies. AI garnered the attention of tech experts and became one of the most engaging topics for research operations. Soon, tech enthusiasts got involved in unleashing the core concepts related to the field.
A good machine learning expert understands the potential of the new-age technology and so strives to explore every concept related to it. This post will discuss one of the widely used machine learning algorithms, the back-propagation algorithm. But, first, let us have a brief intro to machine learning.
What is Machine Learning?
In technical terms, machine learning is a popular discipline of Artificial Intelligence and computer science that focuses on leveraging data and algorithms to continuously mimic how people learn to increase accuracy. Machine learning is an integral part of the ever-expanding field of data science that promises to trigger a revolution in the global technological space. Algorithms are developed to provide close to approximate classifications based on statistical strategies, thus disclosing significant insights.
These insights play an essential role in the decision-making process within organizations and applications, intending to optimize critical growth indicators. As the usage of big data expands, so will the need for machine learning professionals to help identify the most pressing business issues and the data required to address them.
What is the Back-Propagation Algorithm?
A neural network is a collection of interconnected I/O devices, with each connection having a weight. This idea finds its inspiration within the human neurological system. For example, it helps with image perception, human learning, computer speech, etc. In addition, it facilitates the creation of prediction models from large databases.
Back-propagation (backprop, BP) is a popular approach for training feedforward neural networks in machine learning. In addition, many artificial neural networks (ANNs) and functions have resulted from back-propagation. Precisely, all of these techniques are referred to as “back-propagation.” Back-propagation evaluates the gradient of the loss function with consideration to the network’s weights for a single I/O sample fast, instead of a primary direct calculation of the gradient concerning each weight sequentially. Gradient descent, or variations such as stochastic gradient descent, are often used to train multilayer networks and update weights to minimize loss because of their efficiency.
The back-propagation algorithm functions by evaluating the gradient of the loss function of each weight using the chain rule. Also, as the name suggests, the back-propagation algorithm reiterates backward from the last layer to avoid unnecessary evaluation of intermediate terms; hence, referred to as dynamic programming. The evaluation process follows the chain rule.
Back-propagation is a specific example of reverse accumulation. It generalizes the gradient calculation in the delta rule, a single-layer form of back-propagation (or “reverse mode”). Technically, it adheres to gradient evaluation methodology and is sometimes confused as the complete learning process, similar to stochastic gradient descent.
History of Back-Propagation
- J. Kelly, Henry Arthur, and E. Bryson developed the basic notion of continuous back-propagation in the framework of control theory in 1961.
- Bryson and Ho presented a multi-stage dynamic system optimization approach in 1969.
- Werbos proposed to use this approach in an artificial neural network in 1974.
- Hopfield presented his neural network concept in 1982.
- Back-propagation was initially discovered in 1986 by David E. Rumelhart, Ronald J. Williams, and Geoffrey E. Hinton.
- Wan was the first person to use the back-propagation method to win an international pattern recognition competition in 1993.
Categorization of Backpropagation Network
Back-propagation networks have two types:
- Static back-propagation
- Recurrent back-propagation
Static back-Propagation
This type of back-propagation network generates a static input to static output mapping. It helps solve problems like optical character recognition that need static categorization.
Recurrent Back-Propagation
In data mining, recurrent back-propagation feeds forward until a fixed value is reached. The mistake is then calculated and transmitted backward.
A certified machine learning expert knows the difference between the two back-propagation network types and chooses either of them depending upon the work. The critical difference between these two approaches is that in static back-propagation, the mapping is fast, but in recurrent back-propagation, it is non-static.
Advantages of Back-Propagation
Back-propagation is a potential technique used for training the neural network for a specific dataset. It serves the following advantages:
- It is easy to operate and put to use as the users need not have to possess a prior understanding of the network.
- Convenient infrastructure, tools, and interface make it highly adaptable for even novel users.
- Back-propagation is recognized as an effective strategy for achieving the desired technical results.
- It is simple, speedy, and easy to program.
- Only the input numbers are tweaked, leaving all other parameters untouched.
- It does not necessitate the user’s learning of certain functions.
Disadvantages of Back-Propagation
Back-propagation has the following drawbacks:
- First, the algorithm’s performance is highly dependent on the input data.
- Secondly, the training period for learners can get lengthy sometimes.
- Third, it may be affected by noisy data and irregularities.
- Finally, it requires a matrix-based back-propagation approach rather than a mini-batch method.
Back-Propagation’s Applications
Some of the use-cases of the back-propagation algorithm are:
- The back-propagation technique helps train neural networks to recognize each letter of a word or a phrase.
- The technique is widely used in the field of speech recognition.
- Black-propagation technique has significant use in the domain of facial and character recognition.
Back-Propagation Algorithm in a Nutshell
- It is one of the popular approaches for training feedforward neural networks.
- Back-propagation closely follows gradient assessment methods rather than how the gradient is used.
- To simplify the network topology, weighted links with the least impact on the training network are employed.
- To build the link between the input and hidden unit layers, you must analyze a collection of input and activation values.
- It aids in determining the effect of a particular input variable on a network output. Therefore, the rules should reflect the study’s findings.
- Back-propagation is helpful for error-prone tasks like image or speech recognition when using deep neural networks.
- The chain utilization in back-propagation and power rules allows it to function with any number of outputs.
Conclusion
We live in the era of technological advancement, innovation, and development. Therefore, it becomes crucial for us to stay updated with the latest developments in the industry. As AI continues to rule experts’ hearts, concepts like machine learning and Back-propagation are becoming targets for experimentation and research. The technique is a crucial process for neural networks in machine learning. It forms an inevitable part of the diverse machine learning infrastructure. Thus, gaining knowledge about it can help you build your career in the fast-growing tech space. So, if you aspire to become a professional in the machine learning domain, you can enroll in one of the machine learning certification offered by various trusted platforms.
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