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Learning the Top Artificial Intelligence Frameworks
- A scalable multi programming interface for easy programming
- Strong growth drivers, with a strong open source community
- Provides extensive and well-documented manuals for people
- The language used by tensor flow is Python, which is very popular nowadays.
- This framework is capable of high computational power. Hence, it can be used on any CPU or GPU.
- Uses computational graph abstraction to create machine models
- To make a decision or prediction, the framework passes the input data through multiple nodes. This can be time-consuming.
- It also lacks many of the pre-trained models of AI.
- Highly optimized to provide efficiency, scalability, speed, and high-level integrations
- Has built-in components such as hyperparameter tuning, supervised learning models, reinforcement, CNN, RNN, etc.
- Resources are utilized to provide the best efficiency.
- Own networks that can be expressed efficiently such as full APIs, both high level and low level
- As it supports Python and C++, this framework can work with multiple servers at once and hence makes the learning process quicker.
- It has been developed keeping in mind about the recent developments in the world of AI. Microsft CNTK's architecture supports GAN, RNN, and CNN.
- It permits distributed training to train machine models effectively.
- All of its models are written in plaintext schemas
- Offers massive speed and highly efficient work since it is already preloaded.
- An active open source community for discussion and collaborative code.
- Evaluation of expressions are faster due to dynamic code generation
- Provides an excellent accuracy ratio even when values are minimal.
- Unit testing is a significant feature of Theano, as it allows the user to self-verify their code as well as detect and diagnose errors easily.
- optimized for the CPU as well as the GPU
- There will be no more update or addition of features to the current version of Theano.
- There are tailored Tools for every level of experience in the AWS even if you are a beginner, data scientist or developer
- Security is of utmost importance, so all data is encrypted
- Provides extensive tools for data analysis and comprehension
- Integrations with all major datasets
- You don't need to write a lot of code with this framework. Instead, it lets you interact with AI-powered framework via API's.
- Commonly used by data scientists, developers and ML researchers.
- It lacks flexibility as the entire framework is abstracted so if you'd like to choose a particular normalization or machine learning algorithm, you can't.
- It also lacks data visualization.
- Features a lot of routines to index, slice, transpose with an N-dimensional array model
- Optimization routines are present, primarily numeric based with neural network models
- GPU support is highly efficient
- Integrates easily with the iOS and Andriod
- Very high flexibility regarding languages and integrations
- High level of speed and GPU utilization efficiency
- Pre-existing models are available to train the data on.
- Documentation is not very clear to the users, so it presents a steeper learning curve
- Lack of code for immediate use so it takes time.
- It is initially based on a programming language called Lua, and not many are aware of it.
- Mature, well-tested codebase, as it was started in 2012
- Provides a comprehensive set of sample models and datasets to get your application started quickly
- It is continuously supported by an active development team.
- This well-documented framework which efficiently handles numerical intensive computation and visualization
- Implementation of algorithms and signal processing can be performed conveniently with this framework.
- It can easily handle numerical optimization and artificial neural networks.
- It is not very well known when compared to other frameworks.
- Its performance is slower compared to other frameworks.
- Known for it's Scala DSL which is mathematically very expressive
- Extends support to multiple backends which are distributed.
- It aids in clustering, collaborative filtering, and classification.
- Its computational operations make use of Java libraries, which is faster.
- Python libraries are not as compatible as Java libraries with this framework.
- Its computational operations are slower than Spark MLib.
- High performance is one of the key elements and is said to be 100 times faster than MapReduce
- Spark is exceptionally versatile and runs in multiple computing environments
- It can process vast amounts of data quickly, as it works on iterative computation.
- It is available in many languages and easily pluggable.
- It cycles large scales of data processing with ease.
- It can be plugged with Hadoop only.
- It is difficult to understand this framework's mechanism, without extensive work on the same