WHAT IS MECHINE LEARNING ?
Machine learning is a subset of AI, which uses algorithms that learn from data to make predictions. These predictions can be generated through supervised learning, where algorithms learn patterns from existing data, or unsupervised learning, where they discover general patterns in data.
machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.
While artificial intelligence encompasses the idea of a machine that can mimic human intelligence, machine learning does not
Machine learning helps the logistics industry optimize shipping and delivery routes, the retail industry personalize shopping experiences and manage inventory, manufacturers automate factories, and helps secure organizations everywhere.
WHAT IS DEEP LEARNING ?
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.
Deep learning can be used in a wide variety of applications, including:
- Image recognition: To identify objects and features in images, such as people, animals, places, etc.
- Natural language processing: To help understand the meaning of text, such as in customer service chatbots and spam filters.
The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.
three deep learning algorithms will help you solve complicated issues related to deep learning: CNNs or Convolutional Neural Networks, LSTMs or Long Short Term Memory Networks and RNNs or Recurrent Neural Networks (RNNs)
DEEP LEARNING VS MACHINE LEARNING ?
| Machine Learning | Deep Learning |
|---|---|
| Machine Learning is a superset of Deep Learning |
Deep Learning is a subset of Machine Learning |
| The data represented in Machine Learning is quite different compared to Deep Learning as it uses structured data |
The data representation used in Deep Learning is quite different as it uses neural networks(ANN). |
| Machine learning consists of thousands of data points. | Big Data: Millions of data points. |
| Outputs: Numerical Value, like classification of the score. |
Anything from numerical values to free-form elements, such as free text and sound. |
| Algorithms are detected by data analysts to examine specific variables in data sets |
Algorithms are largely self-depicted on data analysis once they’re put into production. |
| Training can be performed using the CPU (Central Processing Unit). | A dedicated GPU (Graphics Processing Unit) is required for training. |
| Machine learning systems can be swiftly set up and run, but their effectiveness may be constrained. |
Although they require additional setup time, deep learning algorithms can produce results immediately (although the quality is likely to improve over time as more data becomes available). |
| Machine learning applications are simpler compared to deep learning and can be executed on standard computers. |
Deep learning systems utilize much more powerful hardware and resources. |
| The results of an ML model are easy to explain. | The results of deep learning are difficult to explain. |
| Banks, doctor's offices, and mailboxes all employ machine learning already. | Deep learning technology enables increasingly sophisticated and autonomous algorithms, such as self-driving automobiles or surgical robots. |
Deep Learning :
- Deep Learning models can work with structured and unstructured data both as they rely on the layers of the Artificial neural network.
- Deep learning models are suitable for solving complex problems.
Machine Learning :
- Machine learning models mostly require data in a structured form.
- Machine learning models are suitable for solving simple or bit-complex problems.
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