AI is on everyone's lips and companies should have a realistic view of the meaningful applications of artificial intelligence. Especially in speech and image recognition, AI applications already achieve considerable success and support human-machine interactions.
ANNs show their strengths in correlation analytical methods with strong causality. In medical diagnostics (image recognition of anomalies) it is hard to imagine life without ANNs.
In general, no established prediction method or model can be assumed to be universally superior for all areas of application, decision situations and data structures, so that a situational assessment must be made for the respective decision problem.
One of the most popular programming languages you can use in AI and ML implementations is Python and therefore our preferred choice. There are a large number of Python open source projects with a wide range of users.
We have experienced and appreciated the advantages of Scikit-Learn, especially in the development of commercial applications.
Scikit-learn is a Python-based library of tools. Its use is quite easy with an excellent accessibility. Good usability for data analysis and data mining, regression, clustering, model selection and pre-processing. The developers based it on Matplotlib, NumPy and SciPy. To become a real AI-professional you have to be able to use this library.
For differentiation or classification, we will shortly touch on other exciting AI open source projects in Python.
1. TensorFlow
TensorFlow leads the list of open source AI projects in Python. It is a product of Google and helps developers to create and train machine learning models. TensorFlow enabled them to convert prototypes into working products quickly and efficiently. With TensorFlow you can work on your machine learning projects remotely in the cloud, in the browser or in local applications.
2. Keras
Keras is an accessible API for neural networks. It is based on Python, and you can run it on CNTK, TensorFlow and Theano. It is written in Python and follows best practices to reduce the cognitive load. It makes working on deep learning projects more efficient. You can run Keras in your browser, on Android or iOS via TF Lite as well as via their web API.
3. Theano
With Theano you can optimize, evaluate and define mathematical expressions that contain multidimensional arrays. It can generate dynamic C code for fast evaluation of expressions. Theano allows you to use NumPy.ndarray also in its functions, so that you can effectively use the possibilities of NumPy.
4. Chainer
Chainer supports multiple network architectures, including recurring networks, convnets, recursive networks and feed-forward networks. Apart from that, it allows CUDA computations, so you can use a GPU with very few lines of code.
5. Caffe
Caffe is a product of Berkeley AI research and is an in-depth learning framework that focuses on modularity, speed and expression. It has excellent architecture and speed, as it can process more than 60 million images in a day.
6. Gensim
Gensim analyzes plain text files to understand their semantic structure.
7. PyTorch
PyTorch facilitates the creation of research prototypes. It allows you to transition between graphics modes through TorchScript and provides distributed training that you can scale.
8. Shogun
Shogun is not exclusively based on Python, so you can use it with several other languages like Lua, C#, Java, R and Ruby. It allows you to combine multiple algorithm classes, data representations, and tools, so you can quickly prototype data pipelines.
9. Pylearn2
Pylearn2 is based on Theano and is one of the most widely used machine learning libraries among Python developers. You can use mathematical expressions to write its plugins, while Theano takes care of their stabilization and optimization. Pylearn2 focuses on flexibility and provides an interface for media (images, vectors, etc.) and cross-platform implementations.
10. Nilearn
Nilearn helps with neuroimaging data. It uses Scikit Learning to perform various statistical actions such as decoding, modelling, connectivity analysis and classification. Neuroimaging is an important area in the medical sector and can help solve a variety of problems, such as making better diagnoses with greater accuracy.
11. Numenta
Numenta is based on a neocortex theory called HTM (Hierarchical Temporal Memory). Many people have developed solutions based on HTM and the software. However, a lot of work is being done on this project. HTM is a machine intelligence framework based on neuroscience.
12. PyMC
PyMC uses Bayesian statistical models with algorithms like the Markov chain. It uses NumPy for numerical problems and has a special module for Gaussian processes. You can store traces as plain text, MySQL databases and Python pickles.
13. DEAP
DEAP is an evolutionary calculation framework for testing ideas and creating prototypes. You can work on genetic algorithms with any kind of representation and perform genetic programming using prefix trees. DEAP includes evolutionary strategies, control points that take snapshots and a benchmark module for storing standard test functions. It works amazingly well with SCOOP, multiprocessing and other parallelization solutions.
14. Annoy
Annoy stands for Approximate Nearest Neighbors. It helps you to find the nearest neighbors while using static files as index. With Annoy, you can share an index across different processes, so you do not have to create multiple indexes for each method.