What are the Uses of Deep Learning: How is It Different from Machine Learning?

Deep Learning is a subset of machine learning that consists of neural networks with three or more layers. It teaches computers to do what comes naturally to humans. Furthermore, it helps computer models in performing classification tasks directly from images, text, or sound.

Features of Deep Learning

Deep Learning is capable of solving complex problems like audio processing, image recognition, and many more. It reduces the need for feature extraction and allows users to do automated tasks. In addition, it facilitates parallel computing and results in reducing overheads. DL is also useful in training Models on a huge amount of data and making them better with more data. Furthermore, it is useful in making High-Quality predictions and it works with well-unstructured data like video clips, documents, sensor data, etc. To further know about it, one can visit Deep Learning Training in Delhi. Apart from these, given below are some of the features of DL.

• Huge Number of Resources- It requires advanced Graphical Processing Units for processing heavy workloads.
• A large Number of Layers in the Model- It requires a huge number of layers like input, activation, and output.
• Optimizing Hyper-parameters- Hyperparameters like Batch size, no of layers, and Learning rate help in improving model accuracy.
• Cost Function- It defines the performance of the model in prediction and accuracy.

Uses of 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.
• Autonomous Vehicle- Self-governing vehicles use cameras, sensors, and external information to collect relevant data. After that, the data goes to dl algorithms that direct the vehicle to perform appropriate actions.
• Fake News Detection- It is capable of dealing with fake news issues as it uses complex language detection techniques to classify fraudulent news sources. It gathers information from reliable sources and matches these with news to verify its validity.
• Facial Recognition- This technology is capable of identifying individuals from images and videos by documenting their faces. It records a person’s face and matches it against a database to know their identity.
• Recommendation Systems- Various apps such as Spotify, Amazon, and Netflix use Depp Learning technology to generate appropriate suggestions. This technology process user data and compiles it to extract consumer info.

Difference Between Machine Learning & Deep Learning

Machine Learning is a subset of artificial intelligence that allows computers to perform tasks without explicit programming. It makes a computer capable of sorting and acting on new data without the need for further human intervention. Machine learning needs more ongoing human intervention to get precise results. It is far less complex than dl algorithms and can often run-on conventional computers.

Deep Learning is an advanced version of Machine Learning that makes computers capable of performing complex tasks like gathering data from an image or video. It is complex to set up deep learning but it requires minimal intervention thereafter. One can only use this technology in powerful hardware and resource with graphical processing units. Many institutes provide Deep Learning Online Training and one can enroll in them to learn this technology and start a career in it.