In today’s, where the amount of data available is bigger than ever, the question of whether it is better to implement deep learning over machine learning has become a popular discussion. It needs to be mentioned that both techniques belong to the same field of artificial intelligence or AI for short, though they are not identical.
In general, It provides some benefits in the presence of large datasets as well as with the challenging problems and the unstructured one. Conventional methods of machine learning have been efficient in many applications but as data and the amount of computations increases, deep learning turns out to be superior.
What is Deep Learning?
It is first necessary to define what deep learning means before discussing the benefits that can come from using deep learning over machine learning. Deep learning is a sub-discipline of machine learning and is designed in the form of neural networks similar to the human brain. The parametric method it uses is artificial neural networks with more than one layer hence referred to as deep learning. It is more useful when there is a large volume of data that is unstructured, including image, audio, and text data. These multiple layers enable the deep learning models to learn features on their own instead of the engineer having to design the features needed on the data set with the traditional machine learning methods.
These models are often developed using platforms such as TensorFlow, PyTorch, or Keras, which allow for the development of complicated neural networks. However, with the machine learning models, there could be basic algorithms such as decision trees, support vector machines, or linear regression.
Some characteristics that define Deep Learning
This decision to choose deep learning over machine learning is due to the following characteristics that makes deep learning more appropriate for some tasks.
Automatic Feature Extraction
In most cases, the feature extraction part is very much dependent on humans when using more conventional machine learning algorithms. Machine learning gets rid of this need into deep learning as during the process of learning, deep learning can get features of learning and can optimize them as well.
Scalability
Another one of the most crucial differences that define deep learning advantaged from the machine learning one is the scale with data feature. Every time the size of data set increases, deep learning models are known to advance whereas the equivalent machine learning models are known to decline or even stagnate if some parameters are not well set.
Handling Unstructured Data
One of the paramount strengths of deep learning over machine learning is its ability to work on raw data type such as image, videos or natural language text. In these contexts, this becomes an obvious choice as traditional machine learning struggles with unstructured data without ‘bootstrapping’ preprocessing.
Complexity Handling
They can operate on elaborate data matrices while inputting highly complex data sets. For instance, in a scenario in which the data points are images or speech, the correlation may be complex. It therefore becomes apparent that deep learning over machine learning is helpful due to the fact that deep neural networks are able to navigate these complications in the network than other conventional algorithms.
Transfer Learning
In case of deep learning, transfer learning is a technique which enables a learner, initially trained on a particular task, to be reused in another related task. Due to this capacity, it becomes capable to choose it in scenarios where labeled data is limited but pre-trained model needs to be fine-tuned for good results.
Parallel Processing
Deeper learning than machine learning is helpful in parallel processing in GPU helps in advancement of training times. Other machine learning models also take advantage of the current and improved hardware but the architecture of the model is optimized to take full advantage of the current and improved hardware.
When it Makes Sense to use Deep Learning Instead of Machine Learning
Even though deep learning has been adopted extensively, there are some cases in which sticking to machine learning is a better decision than moving toward deep learning. Here are a few of the cases where deep learning might work well:
Large Datasets
Deep learning is the first choice over machine learning when it comes to large data sets which are increasing every day. Deep learning is much more powerful when it comes to training on large datasets while in case of traditional machine-learning models its performance saturates as the size grows.
For Unstructured Data
When we are dealing with data which involves images, Videos, speech or even text our choice. Convolutional Neural Networks (CNNs) are good at solving image processing while the Recurrent Neural Networks are suitable for sequential data, like text or speech.
In cases where the level of complexity is high, such as with image classification, natural language processing, or autonomous driving, the data point relations can simply become too intricate for more simple models. This is where it comes in – because neural networks are deep and this allows them to capture patterns that are complex for machine learning.
For real-time processing applications such as video analysis, speech recognition, and fraud detection, models have an upper hand as it’s the efficiency of these models that enables real-time decision-making with accuracy, which makes them a stronger alternative compared to traditional machine learning, especially when working with big data.
When it comes to getting the right answer, deep learning models tend to outperform machine learning algorithms on tasks that hinge on identifying subtle, underlying patterns in complex data. This has especially been the case in domains such as healthcare, where mistakes can cost lives and where, in general, deep learning has taken precedence over machine learning because of its superior performance.
It requires more computational power and longer training times, which can only be afforded when the budget and resources are high. On the other hand, the cost of computing can limit simpler machine learning models.
Conclusion
The reason you keep hearing deep learning over machine learning is that it has outperformed machine learning in some of the most challenging AI problems. And it will only become more prevalent in industries where AI research and product development are important. Otherwise, both are valuable for many other tasks in AI. It proves to be more effective than traditional machine learning because it automatically learns representations from unstructured data (e.g., audio, video, and text), while machine learning requires manual feature engineering. Deep Learning has been demonstrated to be particularly good at making sense of massive amounts of unlabeled or labeled data for classifying high-dimensional & multiple modalities data (e.g., vision, sound), or playing games.
Besides capability, deep learning also fits into today’s software/hardware ecosystem: It relies on modern CPUs & GPUs (and possibly FPGAs) with abundant memory and high compute power on both mobile devices and the cloud; it leverages widely used programming tools & environments, databases etc., can be easily retrained on new related task with mixed supervised, unsupervised training by using, for instance, reinforcement learning or Generative Adversarial Networks, do not require to write tonnes of code so they are easy fast prototyping tools etc.
So as it keeps evolving and benefiting from more research into optimisation algorithms /engineering efforts/papers/books/software/tools/etc up to the point when its popularity will not allow any more dramatic further improvements we believe that increasingly this will be the method of choice for building such systems therefore we hear about it all the time whatever name it gets.
FAQs
When the average layman hears words such as machine learning what does he or she think of?
Some of them are less obvious whereas the most noticeable differences relate to the models’ architecture and complexity. As far as constructing machine learning models is concerned, it can be highly manual intensive with regard to feature engineering; this is not the case with models–since these employ many layers of neural networks and the given method encompasses feature extraction in its nomenclature.
What scenario will warrant using a model than the regular machine learning model?
On the other hand, when working with very big data, text or images or if have a highly complicated problem, it is advisable to try using deep learning mechanism instead of a machine learning one.
Is there a scenario where Deep learning is not useful as compared to the ordinary machine learning?
By and large when the data sets are small, when computational power is a limited or when it is required that the results are explained and justified – in brief the Machine Learning is referred to as the Classical Mechanism.
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