What is Deep Learning?
Deep learning (DL) is an Artificial Intelligence (AI) function that imitates the workings of the human brain in data processing and creating patterns used in decision making.
More precisely Deep Learning is a class of machine-learning in the form of a Neural Network that uses a cascade of layers of processing units to extract features from data and make predictive guesses for the newest data.
The conceptual definition of Deep Learning can be stated as, a technology used for recognizing objects.
The system is “dumb” because it cannot do anything on its own. The system learns with big data and trial and error guesses to adjust weights and bias to establish key features.
It is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sounds, and texts.
Why is it called Deep Learning?
Deep – Cascading layers of processing. Deep networks refers to +3 layers of cascading.
Learning – Algorithms “learn” from data by modelling features and updating probability weights assigned to feature nodes in testing how relevant specific features are in determining the general type of item.
Types of DL.
- Supervised Learning.
- Unsupervised Learning.
Supervised learning means learning patterns from classifying labeled data.
Unsupervised learning means learning pattern from unlabelled data.
Early success in deep learning is seen in the Youtube search algorithm. If you ever notice while searching on youtube, if we want to search a song and we type any lyrics in between the song, youtube gives us the perfect result.
Uses of Neural Networks in DL?
As we’ve mentioned Deep learning consists of neural networks. Neural networks helps in developing it. Mainly Two types of neural networks are used.
- Convolution Neural Network.
- Recurrent Neural Network.
Convolution Neural Network helps in image recognition. Convolve roll up to higher levels of abstraction in feature sets.
The Recurrent Neural Network helps in speech, text, and audio recognition. Recurring iterate over sequential inputs with memory function LSTM (Long short term memory) remembers sequences and avoids gradient vanishing.
How does it work?
Deep learning is inspired by cognitive functions of human brain. The first hierarchy of neurons that receives information in the visual cortex is sensitive to specific edges while brain region further down the visual pipeline which is sensitive to more complex structures such as faces.
A deep neural network consists of a hierarchy of layers, whereby each layer transforms the input data into more abstract representations. The output layer combines those features to make predictions.
It is used for speech/audio processing, computer vision, and Natural language processing.
It consists of one input and one output layer and multiple fully connected hidden layers in between. Each layer is represented as a series of neurons and progressively extracts higher and higher-level features of the input until the final layer essentially makes a decision about what the input shows.
It learns by generating an error signal that measures the difference between the predictions of the network and desired values and then using the error signal to change the weights so that predictions will be more accurate.
Applications of Deep Learning.
- Speech Recognition.
- Giving command to your phone and iot devices is possible due to Deep learning.
- Image Recognition.
- Through the image, the person, objects or attributes of a specific can be predicted. ex – Google lens.
- Customer relationship management.
- Chat – Bots are used for greeting and replying small customer problems
- Bioinformatics is used to identify and recognize a person on the basis of their fingerprints, Iris scan, etc
- Mobile Advertising.
- With the help of DL, Enterprises are able to find a specific audience for marketing their products.
In our above article, we tried to explain about what DL is, how it works, and its applications. As we all know how Artificial intelligence is consider of the post-digital Era. and Deep learning plays a great role in making artificial intelligence so reliable. In the application, we gave according to that you can consider how we use deep learning in our day to day life and how it makes our life simple. DL has its cons, its algorithm works on predictions and it cannot be accurate. there always will be the slightest error.