The authors of [1] used Recurrent Neural Network (RNN) model which produced state of the art performance on the user purchase prediction problem in Ecommerce without using explicit features. The model is straightforward to implement and generalizes to different datasets with comparable performance. RNN & its variants LSTM, GRU (Gated Recurrent Units) are widely available in both open source projects & commercial software. For #Matlab users, LSTM is available in the Deep Learning (DL) toolbox, see [2].

Other relevant posts on #CustomerAnalytics are here: https://lnkd.in/gnNNT4S

Abstract:
A neural network for predicting purchasing intent is presented in an Ecommerce setting to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines (GBM). A trainable vector spaces is used to model varied, semi-structured input data comprising categoricals, quantities & unique instances. Multi-layer recurrent neural networks capture both session-local & dataset-global event dependencies and relationships for user sessions of any length.

[1] " Predicting purchasing intent - Automatic Feature Learning using RNN "-pdf
https://lnkd.in/gATYtxj

[2] " MathWorks #DeepLearning Toolbox "
https://lnkd.in/g3r_S9V

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All ***Cheat Sheets*** in one place.

Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R #sql #matlab #datamining #datawarehousing

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Developing the control of an exoskeleton with #Matlab & #Simulink to help patients relearn how to walk. The Statistics & Machine Learning, Curve Fitting Toolboxes were also used.

Students and Mechatronic engineers may want to check out the links in Comment-1 for a Youtube short tutorial on an exoskeleton designed with Simulink plus some research papers that I came across on the topic. There are many, but I simply list 3. See Comment-2 for the link to Lokomat's website (which is mentioned in the MathWorks blog article) to see their exoskeleton products.
https://blogs.mathworks.com/headlines/2017/06/28/robotic-exoskeleton-helps-patients-relearn-how-to-walk/

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"Multi-Label Learning", MLL (https://lnkd.in/ePhb3Fy) is a classification problem where many labels can be assigned to each instance. The more general multiple outputs/targets learning subsumes MLL for categorical targets. Wei Tong, et al, proposed SafeML (safe multi-label) model in [1] for prediction of weakly labeled data, that is when relevant labels of examples are partially known or missing which means the MLL method does not hurt performance when using weakly labeled data. The #Matlab code is in [2].

Other related posts on:
a) #MultiLabelLearning posts are here: https://lnkd.in/g_FhHDq
b) #MultiTargetLearning are here: https://lnkd.in/gxxns3a

Abstract:
Here, the MLL with weakly labeled data, i.e, labels of training examples are incomplete, which commonly occurs in real applications, e.g, image classification, document categorization is studied. This setting includes, e.g, (i) semi-supervised multi-label learning where completely labeled examples are partially known; (ii) weak label learning where relevant labels of examples are partially known; (iii) extended weak label learning where relevant & irrelevant labels of examples are partially known.

[1]"Learning safe multilabel prediction for weakly labeled data"-pdf
https://lnkd.in/g73gWJU

[2]Code
https://lnkd.in/gk4uvcH

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