Difference between ML and DL

Now machine learning and deep learning are at their peak, and their role has grown significantly. In one of our previous articles, we reviewed the benefits of using ML for business on the example of the insurance sector: https://light-it.net/blog/the-role-of-machine-learning-in-insurance-sector/

In 2021, experts have predicted that tools that provide companies with self-service access to behavioral analytics and personalization technology will evolve in commonness and revenue.

Eventually, the articles about machine learning and deep learning are divided into two types: either three-volume works with formulas and tangled mess or tales about artificial intelligence, the professions of the future, and magical data scientists.

I decided to garner all the boatloads of data on these topics to find out how these two subsets of AI differ from each other.

Let’s check the following definitions for understanding deep learning and machine learning.

Deep Learning vs. Machine Learning

Deep learning is a subdivision of machine learning based on a network of algorithms called artificial neural networks.

The process of learning is deep since the structure of artificial neural networks is somewhat complex and comprises the input, output, and hidden layers. Each layer contains units that convert input data into information that the next layer can use for a specific forecasting task. With such a structure, the computer can gain knowledge using its own data processing.

Machine learning is an extensive subdivision of artificial intelligence that studies methods for constructing learning-capable algorithms.

Top applications of Machine Learning across industries:

  •  Social Media 
  •  Insurance
  •  Product Recommendations
  •  Image Recognition
  •  Sentiment Analysis
  •  Automating Employee Access Control
  •  Marine Wildlife Preservation
  •  Regulating Healthcare Efficiency and Medical Services
  •  Predict Potential Heart Failure
  •  Banking Domain
  •  Language Translation

Top applications of Deep Learning across industries

  •  Self Driving Cars
  •  News Aggregation and Fraud News Detection
  •  Natural Language Processing
  •  Virtual Assistants
  •  Agriculture
  •  Garbage Recycling
  •  Entertainment
  •  Visual Recognition
  •  Fraud Detection
  •  Healthcare
  •  Detecting Developmental Delay in Children
  •  Adding sounds to silent movies
  •  Automatic Machine Translation
  •  Demographic and Election Predictions
  •  Deep Dreaming 

The machine learning process is organized by the following steps:

  1.  Sending data to the algorithm. (Feeding algorithm with additional data, such as extracting components.)
  2.  Using this info to train the model.
  3.  Testing and deploying the model.
  4.  Using an expanded model for an automated forecasting task. (Calling and using the deployed model to get the predictions returned by the model.)
  5.  Improving model.

The purpose of ML is to predict the outcome of the input data. It is required “to feed” the machine with varied data, so it will be easier to find patterns and more accurate results.

So, if you want to train the machine, you need three things:

  1.  Data — to detect spam — you need examples of spam emails, predict the stock price — you need a price history, user’s interests — likes or posts. You need as much data as possible. Tens of thousands of examples are the worst minimum for the desperate. 
  2.  Features, properties, characteristics, signs — they can be the type of the car, the gender of the user, the stock price, even the counter of the occurrence of a phrase in the text can be a feature.
  3.  Algorithms — one task can be solved by different methods almost always. The way you choose depends on the accuracy, speed of work, and size of the finished model. But there is one caveat: if the data is low, even the best algorithm won’t help. Don’t get hung up on percentages, but collect more data.

The learning process in Deep Learning is organized in such a way:

  1.  In the neural network, everything revolves around three elements: the input layer (data to analyze) and a minimum of two hidden layers, or nodes, which do the computation using the DL algorithm. 
  2.  The calculated result we get in the output layer.
  3.  The most interesting happens in the hidden layers where the calculation is performed, including the input data and a path defined by the Activation Function.
  4.  If the outcome is not the one we expected, the connections’ weight is recalibrated, and the analysis is rerun. This process is repeated until the result is as accurate as possible.
  5.  Hyperparameter Tuning – trying to improve the positive results by revisiting the training step.  

If you train the neural network via Deep Learning, you need:

  1.  An overreaching amount of dataset — from 1 million samples.
  2.  A large volume of computational power — computers that use accelerators such as GPU or field-programmable gate arrays (FPGA).

What is the core difference between Machine Learning and Deep Learning?

ML algorithms can understand labeled and structured data and then use it for generating new results with immense numbers of datasets. Yet, when the outcome turns out to be invalid, it becomes crucial to retrain the algorithms.

Deep learning networks as opposed to ML can act without human supervision, as multi-tiered layers store data in a decision tree of various concepts to analyze and learn from their own mistakes. Yet, even they can be inaccurate if the data quality is not adequate. Data is everything, and in the end, it defines the quality of the result.

Considering that machine learning algorithms depend upon structured and labeled data, they aren’t a good fit for processing complex queries with vast amounts of datasets.

Deep learning works well to address large-scale problems. 

Analyzing all these structures with layers, hierarchies, and concepts the neural networks handle, it is better to use Deep learning for performing complex calculations, not simple ones.

When to apply Deep Learning in business?

  1.  If you have massive volumes of data.
  2.  If problems you need to solve are too heavy for machine learning.
  3.  If you have enough computing resources and can manage the required hardware and software for training neural networks.

When to apply Machine Learning in Business?

  1.  If you have structured data that can be to train ML algorithms.
  2.  If you want to take advantage of AI to outrun the competition.

 Machine learning solutions can help automate various business operations, including identity verification, advertising, marketing, intelligence gathering, and leveraging great future opportunities.

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