Tutorial by Topics: learn

Deep Learning is a sub-field of machine learning where multi-layer artificial neural networks are used for learning purpose. Deep Learning has found lots of great implementations, e.g. Speech Recognition, Subtitles on Youtube, Amazon recommendation, and so on. For additional information there is a dedicated topic to deep-learning.

Apache spark MLib provides (JAVA, R, PYTHON, SCALA) 1.) Various Machine learning algorithms on regression, classification, clustering, collaborative filtering which are mostly used approaches in Machine learning. 2.) It supports feature extraction, transformation etc. 3.) It allows data practitioners to solve their machine learning problems (as well as graph computation, streaming, and real-time interactive query processing) interactively and at much greater scale.

Kibana is front end data visualization tool for elasticsearch. for installing kibana refer to the kibana documentation. For running kibana on localhost go to https://localhost:5601 and go to kibana console.

This topic includes short, brief but comprehensive examples of loading pre-trained weights, inserting new layers on top or in the middle of pre-tained ones, and training a new network with partly pre-trained weights. An example for each of out-of-the-box pre-trained networks, available in Keras library (VGG, ResNet, Inception, Xception, MobileNet), is required.

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