Any one of the following methods can be used to install it: TensorFlow can be installed with binary packages or authorised GitHub sources. TensorFlow was released as open source in November 2015 by Google and this move has motivated research scholars, academicians and scientists to work on this powerful library. It is a second generation system developed by Google after DistBelief. TensorFlow has been developed by Google under a research project for deep learning titled Google Brain. It is based on Python and C++ at the back-end, with the incorporation of algorithms for data flow as well as graphs based numerical computations to achieve multi-layered computations with higher accuracy and a lower error rate.
TensorFlow ( ) is a powerful open source software library for the implementation of deep learning. TensorFlow: A Python based open source software library for deep learning Other branches of deep learning include deep structured learning, deep machine learning or hierarchical learning.ĭeep learning can be used for various real world applications including speech recognition, malware detection and classification, natural language processing, bioinformatics, computer vision and many others. Figure 1: TensorFlow logoĭeep learning is one of the branches of machine learning with a strong base of algorithms that have multi-layered processing, a higher degree of computations and accuracy, and a lower error rate with the integration of deep graph based learning. To implement modelling and training with ANN, there are a number of open source tools available including SciLab, OpenNN, FANN, PyBrain and many others.
ANN based learning is fully dependent on the dataset used for training the model and apparently, if the dataset is not accurate, the predictive analysis will affect the accuracy of the results. ANN can be used for malware detection or classification, face recognition, fingerprint or finger vein structure analysis in which the previous dataset is used for training a model and then the prediction or classification of further datasets is done. Metaheuristics, in turn, has many optimisations like Ant Colony Optimisation, Cuckoo Search, Bees Algorithm, Particle Swarm Optimisation, etc.ĭespite the number of metaheuristic approaches and other effective soft computing algorithms, there are many applications for which a higher degree of accuracy and lower error rate is required.įor machine learning, the approaches of artificial neural networks (ANN) or support vector machines (SVM) can be used with the dataset to be integrated. There are a number of prominent soft computing approaches such as neural networks, fuzzy logic, support vector machines, swarm intelligence, metaheuristics, etc. Generally, these operations are performed using some metaheuristic approach in which global optimisation or simply effective results can be fetched from a huge search space of solutions.
Machine learning has core tasks associated with classification and recognition, which are usually related to artificial intelligence. Nowadays, a number of tools and technologies are available for the implementation of data mining and machine learning including WEKA, Tanagra, Orange, ELKI, KNIME, RapidMiner, R and many others which have Java, Python or C++ for back-end programming and customisation.