Artificial intelligence (AI) continues to be technology’s hot topic, growing exponentially in recent years. Many of the recent breakthroughs are the result of deep learning frameworks, where an app learns skills from an annotated dataset during training cycles. Also called machine learning, the framework applies this new knowledge to process new data.
When it comes to AI, you can choose from many different deep learning frameworks, depending on your needs. We’ll discuss some of the major ones in this blog, and outline each framework’s pros and cons.
Tensor Flow
Developed by Google, TensorFlow provides a robust, open-source framework that supports deep learning. TensorFlow can be used in many programming languages, notably Python, Javascript, C++, and Java.
This open-source framework is capable of high computational power, and it can run on different CPUs or GPUs. That flexibility offers benefits for many different types of applications.
Features
- Scalable multi-programming interface for easy programming
- Strong open-source community
- Extensive and well-documented manuals
Pros
- Uses Python
- Capable of high computational power and can be used on any CPU or GPU
- Uses computational graph abstraction to create machine models
Cons
- Making a decision or prediction can be time-consuming as the framework passes the data through multiple nodes.
- Lacks many of the pre-trained models of Artificial Intelligence
Caffe
Developed at the University of California, Berkeley, Caffe (Convolutional Architecture for Fast Feature Embedding) supports different types of deep learning architectures for image classification and segmentation. It is written in C++, with a Python interface.
Caffe includes a preloaded set of trained neural networks, making it particularly useful for tight deadlines. This open-source deep learning framework is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.
Features
- All of its models are written in plaintext schemas
- Preloaded, so it’s fast and efficient
- Active open-source community
Pros
- Interlinks C, C++, and Python
- Supports the modeling of convolutional neural networks (CNN)
- Efficient when computing numerical tasks
Cons
- Not capable of handling complex data
Scikit Learn
As the most used library in Python for data mining and data analysis, Scikit Learn provides a general-purpose machine learning framework designed for supervised and unsupervised machine learning algorithms. It’s a community effort, and anyone can contribute to it.
Scikit Learn offers a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python. The framework is built on top of several popular Python packages such as NumPy, SciPy, and matplotlib, each responsible for lower-level data science tasks.
Features
- Supervised and unsupervised learning algorithms
- Feature extraction from text and images
- Cross-validation
- Dimensionality reduction
- Clustering
- Ensemble methods
Pros
- Free and easy to use
- Versatile for real-world purposes like consumer behavior prediction, creation of neuroimages, etc.
- Online community support
- Good API documentation
Cons
- There are better choices for in-depth learning
Keras
Because of its ease of use and focus on user experience, Keras is the deep learning solution of choice for many university courses. Keras also includes backend support for Tensorflow, Theano, CNTK, and more.
Keras is used by scientific organizations around the world. It offers low-level flexibility to implement arbitrary research ideas with optional high-level convenience features to speed up experimentation cycles.
Features
- Follows best practices for reducing cognitive load
- Consistent and simple APIs
- Minimizes the number of user actions required for common use cases
- Clear and actionable error messages
- Extensive documentation and developer guides
Pros
- User-friendly and fast deployment
- Quality documentation and community support
- Multiple backend and modularity
- Pretrained models
- Multiple GPU support
Cons
- Problems in low-level API
- Need improvement in some features
Microsoft CNTK
CNTK, the Microsoft Cognitive Toolkit, is a framework for deep learning. A computational network defines the function to be learned as a directed graph where each leaf node consists of an input value or parameter, and each non-leaf node represents a matrix or tensor operation upon its children.
This open-source framework supports text, message, and voice remodeling. Microsoft CNTK provides an efficient scaling environment due to a faster overall evaluation of the machine models. It’s the leading choice for applications like Skype, Cortana, etc.
Features
- Provides efficiency, scalability, speed, and high-level integrations
- Includes hyperparameter tuning, supervised learning models, reinforcement, CNN, RNN, etc.
- Networks can be expressed efficiently such as full APIs
Pros
- Supports Python and C++
- Works with multiple servers at once to make the learning process quicker
- Supports GAN, RNN, and CNN
- Permits distributed training to train machine models effectively
Cons
- Lacks a visualization board and mobile ARM support
Theano
Using GPUs in place of CPU, this framework supports deep learning research and is capable of delivering accuracy for networks that need high computational power like multi-dimensional arrays. Theano is based on Python, which provides faster processing and response.
Features
- Fast evaluation of expressions
- Dynamic code generation
- Accuracy ratios
- Unit testing
Pros
- Offers support for data-intensive applications through combinations with other libraries
- It is efficiently optimized for the CPU as well as the GPU
Cons
- There will be no more updates or addition of features to the current version of Theano.
Amazon Machine Learning
This framework already has existing user bases in AWS, S3, and Amazon Redshift. This is a managed service by Amazon with data analysis, training of the model, and evaluation.
Features
- Tailored tools for every level of experience
- Data encryption
- Extensive tools for data analysis and comprehension
- Integrations with all major datasets
Pros
- APIs
- Commonly used by data scientists, developers, and machine learning researchers
Cons
- Lacks flexibility
- Lacks data visualization
Apache Mahout
Apache Mahout assists with the development of scalable machine-learning frameworks. It doesn’t deal with APIs as such, but it helps in the implementation of new machine-learning algorithms by data scientists and engineers.
Features
- Known for its Scala DSL, which is mathematically very expressive
- Extends support to multiple backends
Pros
- Aids in clustering, collaborative filtering, and classification
- Computational operations make use of Java libraries
Cons
- Python libraries are not as compatible as Java libraries with this framework
- Its computational operations are slower than other frameworks
Torch
Torch is an open-source framework that supports numerical operations. It offers numerous algorithms for the faster development of deep learning networks. It is used extensively in the AI labs of Facebook and Twitter.
Features
- Routines to index, slice, and transpose with an N-dimensional array model
- Optimization routines are present, primarily numeric based on neural network models
- GPU support is highly efficient
- Integrates easily with iOS and Android
Pros
- Very high flexibility regarding languages and integrations
- High level of speed and GPU utilization efficiency
- Pre-existing models are available for data training
Cons
- Steeper learning curve
- Based on a lesser-known programming language called Lua
Frequently Asked Questions
Is TensorFlow a framework?
TensorFlow is an open-source framework developed by Google researchers to run machine learning, deep learning, and other statistical and predictive analytics workloads. It’s designed to streamline the process of developing and executing advanced analytics applications for users such as data scientists, statisticians, and predictive modelers.
What is an Artificial Intelligence framework?
An Artificial Intelligence framework allows for the easier and faster creation of artificial intelligence applications. These include machine learning, deep learning, neural networks, and natural language processing solutions.
What are the types of Artificial Intelligence modeling?
An AI model is a program or algorithm that uses data to recognize certain patterns. This allows the model to reach a conclusion or make a prediction when provided with sufficient information. Types of Artificial Intelligence models include:
- Linear Regression
- Deep Neural Networks
- Logistic Regression
- Decision Trees
- Linear Discriminant Analysis
- Naive Bayes
- Support Vector Machines
- Learning Vector Quantization
- K-nearest Neighbors
- Random Forest
