Have you ever wondered,
How do large conglomerates and businesses make decisions?
Do they make predictions for future investments?
Do they take intuition-based decisions?
Well, Large organizations invest heavily in technology to make their investments and make better decisions. And, one such technology is Deep Learning. Deep Learning is a subfield of machine learning that is used to create neural networks and train them on large data. It is used to find patterns from old data and make decisions based on that result. This domain of AI is growing at a very rapid pace. And if you are interested in this field, or want to be a data scientist then you should know the top deep learning tools.
Deep learning tools are used by data scientists to create models that can learn like the human brain and process data and patterns to make better decisions. If you are interested in top deep learning tools then you can refer to this article, But before let’s understand what is deep learning and why should you learn it.
Table of Content
What is Deep Learning?
Deep learning is a sub-field of machine learning, used to train neural networks with multiple layers of training to extract useful patterns and information from the existing data. These networks are used by large-scale companies to generate predictions which are very helpful in taking decisions. Some of the most common applications in which deep learning operates are natural language processing and speech recognition.
Deep Learning can be used for techniques like supervised, unsupervised, and reinforcement learning to make predictions. These are the techniques of machine learning and we can use that because Deep learning is a sub-field of Machine learning itself. But there are some differences between machine learning and Deep learning like
- Deep learning requires a large amount of data compared to machine learning.
- Deep learning takes more time to train data.
- Deep learning requires a high-processing computer with GPU compared to machine learning.
- Deep learning is preferred for complex tasks.
To learn more about Deep learning, refer to – Introduction to Deep Learning
Top 10 Deep Learning Tools
Deep Learning Tools are software that is used in the process of building Deep Learning models, which involve all the steps including model creation, loading data, training, evaluating, and making predictions.
Below are some of the best Deep Learning tools used by developers to create and train models. To learn deep learning for creating better AI applications, you should have explored the below deep learning tools.
1. TensorFlow
Tensorflow is a free and open-source library of Python and C++, which is used in machine learning, But it is mainly used in Deep learning and is considered one of the best Deep Learning Tools for creating AI applications. It is used to create large-scale neural networks with multiple layers due to its faster compilation time. It is mainly used for object detection like image recognition and allows you to create ANN and CNN models for computer vision, text classification, image processing, and speech recognition. It has a large community support. Many large-scale companies use TensorFlow, such as Airbnb, Uber, and eBay.
To learn more, refer to this article – TensorFlow
Features
- Scalability for Handling Complex and Large-scale Data
- High Performance for efficiency and computations is fast
- Flexibility in Design and Implementation for various Use Cases
- Ease of Deployment and integration into various environments
- Large community support
2. Pytorch
Pytorch is a free and open-source Python framework based on the Torch library used in Machine learning and Deep learning. It is used in applications of Deep learning such as computer vision and Natural Language Processing. Pytorch take tensors and makes it easy to move them to GPUs for faster processing while creating a model and training a neural network in the process of creating an AI application. This Deep learning tool is more flexible with Python and the Numpy stack.
To learn more, refer to this article – Pytorch
Features
- Dynamic Computational Graph
- User-friendly API
- Community Support and large resources
- Seamless Integration with other libraries
- Easy to Use for distributed training
3. Keras
Keras is an open-source library of Python that provides an amazing interface to create Deep learning models. It uses Tensorflow or Theano in its backend. It provides a high-level abstraction to build neural networks and create more accurate and fast models. As its interface is more simple and more useful in offering feedback on errors. This deep learning tool supports recurrent neural networks and convolutional neural networks. The Keras library also includes pre-trained models, called Keras Applications, for various computer vision tasks.
To learn more, refer to this article – Keras
Features
- Flexibility to work with multiple backends and architecture.
- Large collections of pre-trained models for various tasks.
- Simple and Easy Integration with Tensorflow and other frameworks.
- User-friendly and high-level API for building Deep learning models.
- Rich in resources and community support.
4. OpenNN
OpenNN is a free and open-source library of C++, for implementing neural networks and creating, training models for deep learning, and finding patterns to take decisions. It is much more efficient in faster memory allocation and execution as well. This Deep learning tool provides a wide range of functionalities to support different types of neural networks like feedforward neural networks and radial basis function networks. This kind of diversity allows users to cover a wide range of Machine learning tasks, such as classification, regression, and time series predictions.
Features
- Comprehensive support for various types of artificial neural networks.
- Emphasis on high performance and efficiency through optimizations.
- Flexible in covering different machine learning tasks.
- Data preprocessing and Post-processing.
- Large Community and rich in resources.
5. Theano
Theano is a Python library that is used for evaluating mathematical expressions and matrix-values expressions. And, these computations are expressed using a NumPy-esque syntax. It is commonly used in making deep learning-based applications. It Performs faster on GPU as compared to CPU. This Deep learning tool is so much popular because it is widely used to handle the different types of computation required in large neural network algorithms.
To learn more, refer to this article – Theano
Features
- Efficient and flexible symbolic mathematical expression library.
- Seamless integration with NumPy for numerical computations.
- Supports GPU acceleration for high-performance computing.
- Optimization for deep learning and neural network implementations.
- Open-source machine learning framework.
6. H2O.ai
H2O is a scalable and open-source deep learning tool that is a part of the H2O.ai open-source machine learning Platform. It is built using Java as its core technology. We can integrate H2O.ai with other products like Apache Hadoop and Spark as well. It is very easy to use and allow anyone to implement machine learning and predictive analytics and businesses can solve their hard and complex problems by finding pattern from the old data and take necessary decisions. It is much useful for real-time data scoring.
Features
- Scalable and efficient distributed machine learning platform.
- Complete Package of machine learning algorithms and tools.
- Interactive data visualization.
- Seamless integration with big data frameworks and data sources.
- User-friendly interface.
7. DeeplearningKit
DeeplearningKit is an open-source framework of deep learning which is compatible with multiple operating systems and uses metal for GPU acceleration. This deep learning tool is developed in Swift and can perform low-latency deep learning calculations. It supports Convolutional Neural Networks too. In the future, it can also support other deep learning tools.
Features
- Extensive support for popular neural network architectures and models.
- Efficient training on CPU and GPU for faster computations.
- Seamless integration with TensorFlow, Keras, and PyTorch.
- Interactive visualization tools for model monitoring and analysis.
8. Caffe
Caffe is an open-source deep learning framework built in C++ but has an interface in Python. It is used in sever deep learning applications such as computer vision, natural language processing, and multimedia. Caffe is very fast and can process over 60 million images per day with a single NVIDIA K40 GPU. It is very easy to use and in most cases, there is no requirement for coding. There are pre-trained models and ready-to-use templates of networks for most cases.
Features
- High-performance deep learning framework with GPU acceleration support.
- Extensive collection of pre-trained models for various computer vision tasks.
- Flexible and modular architecture for easy model design and experimentation.
- Active Community Support.
- Seamless integration with C++ and Python for efficient development.
9. CNTK
CNTK also known as Microsoft Cognitive Toolkit, is a free and open-source, commercial-grade toolkit using which users can learn deep learning systems. With the help of this deep learning tool data scientists can create multiple and efficient neural networks for taking decisions, some of the types are Convolutional neural Networks and feed-forward neural networks. Microsoft provides a comprehensive tool in the AI market to help developers build more accurate models and networks.
Features
- Scalable Deep Learning Framework Developed by Microsoft
- Efficient Multi-GPU and Distributed Training for High-Performance Computing
- Support for Multiple Programming Languages and Operating Systems
- Rich Library of Built-in Neural Network Layers and Optimizers
- Smooth Integration with Microsoft Azure and Other Microsoft Services
10. MXNet
Apache MXnet is an open-source deep learning framework that is used to train and deploy neural networks in the process of creating large-scale artificial intelligence applications. Businesses create these applications with the help of this deep learning tool, to take critical decisions by training the networks with large amounts of past data so that the pattern finding will be easy for the model. It supports multiple programming languages to create models and also contained pre-trained models as well.
Features
- Scalability with efficient performance.
- Multiple language support.
- Can be deployed on Multiple Platforms.
- Flexible Neural Network for diverse architecture.
- Include auto-differentiation for easy model training.
Conclusion
As you know the exponential growth of Artificial Intelligence and Machine learning, one of its domains has also gained significant traction in recent years and that is Deep learning, it creates neural networks which are trained by large amounts of data to work like a human brain. If you are a data scientist or a developer looking to work in the domain of deep learning then you should know the top deep learning tools for the creation of better and more efficient ai applications.
FAQs
What are the top Deep Learning Tools?
These are some of the best Deep learning tools
1. TensorFlow
2. PyTorch
3. Keras
4. OpenNN
5. Theano
Does Apple use PyTorch?
Yes, Apple uses the PyTorch framework in the MPS backend, providing scripts and capabilities to set up and run operations on Mac.
Which is better TensorFlow or PyTorch?
Most data Scientists prefer TensorFlow over Pytorch due to the easy performance of higher-level operations in Tensorflow. But the performance of Python is much faster in PyTorch.