What is Deep learning?
Deep learning is a kind of artificial intelligence function which imitates the exact working of the human brain in processing data and creating patterns used in decision making. It is basically a part of Machine Learning. Deep learning algorithms basically use such networks which are capable of learning without supervision from data that is unstructured or unlabeled.
Here’s why deep learning can make other machine learning algorithms obsolete:
The question whether deep learning will make other machine learning algorithms obsolete or not is pretty debatable. Some people believe that deep learning can actually provide such supervised learning solutions in the asymptote of training data size, which will push some learning algorithms to near extinction. Statistics have shown that deep learning provides the best predictive power in case of large datasets. Here’s a brief list of reasons why deep learning (machine learning) algorithms might render other algorithms obsolete:
- The Machine Learning algorithms which use connectionist architectures are extremely saturated. These can therefore render regular algorithms obsolete.
- The new tricks that might evolve in future can significantly improve the performance of Deep Learning in the coming years.
- Even if new algorithms are developed, they will take time to learn pattern recognition as deep learning. Deep learning will still dominate because it came first.
- Deep learning has brought algorithms close to perfection, the scope of betterment is highly unlikely.
- Deep learning being the first pattern recognition system has gathered community trust. This trust has the potential to render other algorithms obsolete.
Here’s why deep learning algorithms cannot make other algorithms obsolete:
It is completely true that models based on deep learning algorithms are far more superior and accurate to almost all other learning algorithms in every learning task.
There are many problems with deep learning that can be overcome in the near future, they are:
- It requires enormous datasets to learn from. It becomes impossible to generate proper quality algorithms with small datasets using deep learning algorithms.
- There are various models which can compete with Deep Learning algorithms as they don’t need as much data to reach the maximum possible accuracy.
- To make the most out of Deep learning algorithms huge computational and financial resources are required.
- It consumes humongous time to grow a bigger and accurate dataset.
- The hardware required to create Deep Learning algorithms are pretty expensive.
Conclusion:
There are always two sides of a coin. And it cannot be denied that Deep learning algorithms have the potential to make every other algorithm obsolete. But they have their fair share of drawbacks, which can open a window of opportunity for other algorithm to take over their market.
