Machine learning-deep learnArtificial Intelligence (AI) is a powerhouse in the tech industry. Numerous industry leaders are experimenting with AI in the hope of propelling their businesses to the future. This article focuses on a major category of AI, Machine Learning (ML), and its more advanced form, Deep Learning (DL). I’ll cover the basic features of each method and offer a comparison. When you set out to create your own AI project, you will hopefully understand which model is the better fit for you.

What Is Machine Learning (ML)?

A subset of artificial intelligence, designed to allow computer systems to perform tasks without explicit instructions. To achieve this, ML uses algorithms and statistical models to analyze, identify and understand patterns in data. ML is widely used as a tool for automatically performing tasks that humans would struggle to achieve, or to reduce workloads by automating certain aspects of tasks.

Machine learning algorithms are trained with experience and analysis. An ML model is given an input, a certain task to complete, and can make decisions and set a course to achieve this task with minimal human intervention. The more times the model solves the task and produces an output, the result, the more efficient it becomes. 

Machine Learning Algorithms

Notables examples of ML algorithms include:

  • Supervised learning—learns the mapping function from the input to the output to make predictions based on the value labels assigned to data points.
  • Unsupervised learning—learns the mapping function from the input to the output to produce predictions but without no corresponding output variable. 
  • Reinforcement learning—finds the best behavior or path to navigate an unknown environment to achieve an output with reward feedback to determine the best strategy.

What Is Deep Learning?

A subtype of machine learning. This method uses Artificial Neural Networks (ANNs) to extract features or attributes from raw data. Deep learning networks are designed to mimic the functions and connections of the biological neurons inside the human brain.

Deep learning typically involves a Convolutional Neural Network (CNN), a type of ANN that contains multiple layers. The number of layers determines the depth of the network━the more layers the DL model uses, the “deeper” it is. The first layer is fed with an input, it processes that input and uses scales to manipulate the data and produce a new output which is used as an input for the next layer in the chain and so on until the final layer produces an output, the prediction.

Deep learning is relatively new compared to machine learning and is considered state-of-the-art in terms of AI. Fueled by technological advancements in computer hardware, mainly Graphics Processing Unit (GPU), which became stronger and cheaper at the same time, deep learning networks have increased in popularity over the last two decades. 

GPUs, while made for 3D tasks in computing environments such as computer games and 3D modeling, excel at the necessary calculations for deep learning. Thus, scientists, developers, and engineers could leverage GPUs to train and run their DL models more quickly and make them more efficient.

Deep Learning VS Machine Learning

There is much confusion between machine learning and deep learning as they are both subsets of artificial intelligence and be used for similar tasks. The matter is further complicated by the fact that deep learning is technically a form of machine learning, albeit more advanced.

However, for practical purposes, we make a distinction, as there are important differences between the capabilities and requirements of standard ML and DL. To know which is the most relevant technique in any scenario, it is important to learn what these differences are. Here are a few categories for comparison:

#1 Data Dependencies

Performance is one of the major key differences between deep learning and traditional machine learning algorithms. Deep learning algorithms function best with large datasets, which gives DL an advantage in many fields such as object detection and recognition and in healthcare. Machine learning algorithms, on the other hand, are better at handling small datasets and will perform them quicker.

#2Hardware Dependencies

Traditional machine learning algorithms are much less hardware dependant than deep learning algorithms. In general, deep learning algorithms have much more hardware requirements in terms of expensive GPUs (sometimes in a multi-GPU cluster) and large amounts of storage. 

For example, If you train a DL model for object detection, you will use thousands of training images as input for the model which take up large amounts of storage space and strong GPU to run properly.

Deep learning platforms, such as MissingLink, can help you reduce costs and run your deep learning algorithms more efficiently by offering features such as automating experiments and tracking them across multiple machines.

#3 Feature Engineering

A general process to increase the learning of how the algorithm operates. The process uses domain knowledge to create feature extractors that reduce the complexity of the data and make the patterns easier to detect.

#4 Execution Time

Machine learning takes less time to execute compared to deep learning. This is mainly due to the many layers and parameters of the deep learning algorithm. While it typically takes a few minutes and up to several hours to train a machine learning algorithm, deep learning usually takes much more than that and some algorithms can take several weeks to train. 

The time it takes to execute a deep learning algorithm is highly dependant on the efficiency of the deep learning platform, the number of layers in the network, the level of optimization of the model and the strength of the hardware you use to run it.

#5 Interpretability

Deep learning algorithms are harder to interpret compared to machine learning algorithms, due to their more complex architecture, as well as the fact they contain many more parameters. However, deep learning is more powerful and accurate than standard machine learning. 

Normally, you would need to choose the features in the model and understand which features are most appropriate to represent the data. In deep learning, though, you can let the network do the work and figure this out by itself.

Conclusion

Machine learning and deep learning are both useful for training an AI system to perform analytics tasks. Each of these processes has its pros and cons, with the major difference lying in the complexity and computing power involved. Now that you understand more about how they operate, you should be better equipped to start your own AI project based on your algorithm of choice.

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