Keras – a Google product is a high-level API for deep learning and neural networks. It is a Python program and is utilized to make neural networks easier to set up. It also works with more than one backend neural network.
Keras is easy to learn and use because it has a Python frontend, a great deal of abstraction, and multiple backends that can be used for computations. Because of this, Keras is slower than most other frameworks for deep learning, but it is very easy to use for beginners.
You can switch among different backends with Keras. It works with the following frameworks:
- Theano
- Tensorflow
- PlaidML
- CNTK (Microsoft Cognitive Toolkit)
- MXNet
TensorFlow is the only one of these five frameworks that have made Keras its official API.
Keras Uses in AI
Here are some ways Keras is used in the AI field:
1. Classification and Image Recognition:
Keras is often used in image recognition or classification use cases to figure out what objects or animals are in a picture. The most common kind of neural network utilized for these types of work is called Convolutional Neural Network (CNN), and Keras makes it easy to create, train, and assess CNN models.
2. NLP (Natural Language Processing):
Keras can be utilized for many NLP tasks, such as text classification, translation, or sentiment analysis. RNN (Recurrent Neural Networks) and LSTM (Long Short-Term Memory) networks are often used for these tasks, and Keras has built-in support for all these kinds of networks.
3. Speech Recognition:
Keras can be utilized for tasks like turning spoken words into text that a computer can read. Recurrent Neural Networks and Convolutional Neural Networks are often used together in these tasks.
4. Time Series Analysis:
Keras can do time series analysis tasks like forecasting weather patterns or stock prices. Most of the time, Recurrent Neural Networks and LSTM networks are used to accomplish this normally.
5. Recommender Systems:
Keras can be utilized to make recommender systems that make use of users’ preferences to suggest products or services. Deep Matrix Factorization and NCF (Neural Collaborative Filtering) are often used jointly to achieve it.
6. Object Detection:
Keras can find objects in images or videos and determine where they are. Usually, CNN (Convolutional Neural Networks) and object detection techniques like YOLO or SSD are used together.
7. GAN (Generative adversarial networks):
Keras can be used to build and train GANs, which are used to make new images or data that look like a given dataset. Most of the time, Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) are used in conjunction.
8. Video Analysis:
Keras can be utilized to do video analysis tasks like captioning, video segmentation, or action recognition. CNNs and RNNs are usually combined to perform it.
9. Fraud Detection:
Keras can be used to detect fraud, like finding fake financial transactions in data. Most of the time, autoencoders and anomaly detection algorithms are used together.
10. Reinforcement Learning:
Keras can be utilized to build and train reinforcement learning agents, which are used to learn the best actions to perform in a given environment. Most of the time, (DQN) Deep Q-Networks and other deep learning are combined.
Related terms
Anomaly detection Convolutional neural networks Generative adversarial networks Object detection Reinforcement learning YOLO
