Reference and Education

What Is The Difference Between Machine Learning Deep Learning?

To begin with, Deep Learning is a type of machine learning based on artificial neural networks. It is a combination of machine learning algorithms that uses multiple layers to progressively extract higher-level features from raw input.

Uses of Deep Learning

Deep Learning helps in solving complex problems like Audio processing in Amazon Echo, image recognition, etc. It reduces the need for feature extraction and allows users to automate tasks in less time using Keras and Tensorflow. In addition, it facilitates parallel computing and reduces the chances of overheads. DL facilitates high-quality predictions when compared with humans by training tirelessly. It works well with unstructured data like video clips, documents, sensor data, webcam data, etc. Due to these reasons, this technology is useful in several sectors. It facilitates Customer Relationship Management and helps in identifying trends and predictions about customer needs. To further know about it, one can visit Deep Learning Training in Delhi. Given below are some of the applications of DL.

  • Autonomous Vehicles-Deep Learning algorithms learn from experience by using millions of data sets to learn how to act and respond. Therefore, this technology is useful in the development of self-driving vehicles.
  • Fake News Detection-DL extensions are capable of triggering true and false information across the internet. It helps in eliminating all harmful and fraudulent news from online premises.
  • Virtual Assistants-Virtual assistants like Alexa, Google Assistant, and Siri use Deep Learning to reply to your answers. This technology allows them to follow commands like alarms, play music, schedule, and more.
  • Visual Recognition- It is capable of identifying you from many other people in an image. It allows you to sort out someone from an extensively large number of images.
  • Colorization of Images-DL can colorize black-and-white applications. It applies a process to images, intakes grayscale images, and then yields colorized images as output.

Difference Between Machine Learning and Deep Learning

Machine Learning is a subset of AI that provides a system with the ability to learn and improve from experience. It uses data to train and find accurate results and focuses on the development of a computer program. It uses structured data for data representation and consists of thousands of data points. Machine Learning uses various types of automated algorithms for predicting future action from data. It requires CPU for training and involves more human intervention for getting the results. Machine Learning systems are portable and you can easily set them up or down. It is a simpler technology compared to DL and its model takes less time to train.

Deep Learning is a subset of machine learning that distinguishes itself from classical machine learning by the type of data that it works with. Its algorithms are just like machine learning but they consist of many more levels. These networks of the algorithm together are called the artificial neural network and it replicates just like the human brain. It uses neural networks for data representation and consists of millions of data points.

Deep Learning

Deep learning consists of Artificial Neural Networks (ANNs) that are layered. In addition, each layer has multiple neurons responsible for receiving input, computing it, and referring the output to the next layer. Neural Networks refer to the network of neurons or nodes responsible for computing inputs and producing outputs. There are many applications of deep learning technology and some of them are as follows:

  • Financial Fraud Detection- Financial corporations like banks and insurance firms use Deep Learning technology to detect and predict financial frauds. They use DL algorithms to analyze the patterns common to valid transactions.
  • Natural Language Processing- Human language is sophisticated for machines to understand because of alphabets, words, context, and accents. NLP refers to the process of enabling machines to analyze and understand human language.

Deep Learning uses the neural network that passes data through processing layers, for interpreting data features and relations. It requires GPU for training and there is no need for human intervention once it is running. Deep Learning requires additional setup time and they produce precise results immediately. It is much more complex than Machine Learning and its model requires a huge amount of time due to its large data points. Many institutes provide Deep Learning Training in Gurgaon and one can enroll in them to start a career in it.

Conclusion

Deep Learning is a type of machine learning based on artificial neural networks that provide computer systems with self-learning ability. It is much more advanced than Machine Learning and requires much less human intervention once started. It is a complex technology that requires additional setup time to process results immediately. Due to its numerous capabilities, many sectors have started using it. DL is capable of triggering true and false information across the internet. It can colorize black and white images and can identify you from many people in the image. In conclusion, it is a growing technology that will be used in numerous projects and tasks in the upcoming future.

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