The tools in Artificial Intelligence will not only help to develop but also make an important contribution to optimise networks and workflows. There is a list of AI tools:
1. Scikit Learn
This can be used widely in libraries in machine learning community. There are many factors that make it the go-to library for developers like cross-validation, feature extraction, supervised learning algorithm. It also executes in single processor CPU. It is a library built on scipy, which will include matplotib, ipython, Numpy, pandas, sympy etc. There are AI tools which are popular like ML kit, Theano, swift AI, Deeplearning.
2. TensorFlow
TensorFlow is a open source frameworks for developing ML and AI equipped models.It gives many libraries, packages and tools that assists developers build robust applications powered by ML and AI.
This TensorFlow is demanded tool used by ML or AI engineers. It is an open source framework. It is developed by google. It is used to build version machine learning models. TensorFlow is useful to train and run the neural network image recognition. It also helps in natural language processing, digit classification and also much more. The main objective of Tensorflow is just development neural network but Tensorflow will be highlight to reduce the difficulty of implementing computations on large numerical data sets.
3.Theano
It can be covered over Keras, an abnormal state neural systems library that runs in parallel with Theano library. Keras has fundamental favourable position. It is a moderate python library which is profound in discovering. It will keep running over Theano.
It will be created to make actualising detailed learning models as quick and simple as feasible for innovative work.
It will keep running on python 2.7 or 3.5 and also can consistently will execute on GPUs and CPUs.
4. Caffe
It is a structure made with an articulation, speed and measured quality as prime priority.It will be developed by the Berkeley vision and learning center and google’s deep dream will depend on caffe framework.
5. MxNet
It has for trading computation time for memory through forgetful backprop that is very useful for recurrent nets on very long sequences.
- Built with scalability in mind
- There are many cool features like easily writing custom layers in high level languages.
- Almost all major framework, it is not directly governed by major corporation that is healthy situation for an open source community developed framework.
- We have a TVM support, which will further improve the deployment support and allow running on a whole host new device types.
6. Keras
If you like the python way of doing the things Keras is for you. It is a high level library for neural networks using Tensor Flow or Theano as it backend.
The majority of practical problems will be more like
By picking an architecture that is suitable for a problem
For an image recognition problems by using weights trained on Image Net
Configuring a network to optimise the results. Keras is important and provides an abstract structure easily converted to other frameworks, as needed.
7. Pytorch
Pytorch is known to be an AI system that is developed by facebook. Its code is run on Github and its time that has more than 22k stars. It is picking up a great deal energy since 2017 and is relentless reception development.
8.CNTK
CNTK allows users to easily understand and also combine popular model types like feed forward recurrent networks and DNNs convolutional nets. It implements stochastic gradient learning. It has automatic differentiation and parallelisation for all the multiple GPUs and servers.
9. Auto ML
The AutoML is strongest and also fairly recent addition to the arsenal of tools that are available for all the disposal of machine learning engineer. We enter the new realm of meta wherein software helps up build software. AutoML is a library that is used by many machine learning engineers to optimise the models.
Questions
1. Explain the tool Caffe?
2.What are the purpose of using AutoML?
