Deep Learning

Deep Learning Concepts


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Support Vector Machines

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Support Vector Machines A Support Vector Machine (SVM) is a powerful supervised machine learning model used for classification. An SVM makes classifications by defining a decision boundary and then seeing what side of the boundary an unclassified point falls on. Decision boundaries get defined,  by using a training set of classified points.     Decision boundaries exist even when your data has more than two features. If there are three features, the decision boundary is now a plane rather than a line.   As the number of dimensions grows past 3, it becomes very difficult to visualize these points in space. Nonetheless, SVMs can still find a decision boundary. However, rather than being a separating line, or a separating plane, the decision boundary is called a separating hyperplane . Optimal Decision Boundaries In general, we want our decision boundary to be as far away from training points as possible. Maximizing the dis...
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Natural Language Processing

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Step wise instructions to do the NLP projects. Applications of NLP: =============== Sentiment Analysis Topic Modelling Name Entity Recognition Machine Translate Speech Recognition Question Answering Automatic Summarization Chatbots Market Intelligence Text Classification Character Recognition Spell Checking Autocomplete Voice text messaging Spam filters Related keywords on search engines Search Autocomplete Search Autocorrect Virtual Assistant Knowledgebase Support Customer Service Automation Survey Analytics Social Media Monitoring Descriptive Analysis Automatic Insights
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