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                  1.  Which learning approach uses labelled data for training?

                      a. Supervised Learning          b.  Reinforcement Learning    c. Unsupervised Learning
                  2.  The target variables is categorical in ________________________ problem.

                      a. Regression                   b. Clustering                 c. Classification

                  3.  Which algorithmic model would you use when you have to predict a continuous valued output?
                      a. Regression                   b. Clustering                 c. Classification

                  4.  Which of the following is false about Reinforcement Learning?

                      a. Uses Reward Mechanism
                      b. Target is to Maximise the Reward
                      c. Predicts a continuous value as output

                  5.  Clustering is _____________ learning and its goal is to ______________.

                      a. Supervised, Classify data points, into different classes
                      b. Unsupervised, Divide the data, points into different groups
                      c. Unsupervised, Predict the output, based on input data points




              Sub-Categories of Deep Learning

              Deep Learning helps software learn to do tasks by using a lot of data. The machine is given large amounts of
              information, which helps it learn and improve on its own. These smart systems can even create their own rules.
              There are two types of Deep Learning Models: Artificial Neural Networks (ANN) and Convolutional Neural Network
              (CNN).

              Artificial Neural Networks (ANN)


              It is artificially created efficient computing systems designed to simulate the human brain. It includes machine
              learning as part of Artificial Intelligence. An ANN in its training phase is capable of learning by recognising patterns
              in data which is later used to generate the desired output.
              ANN is made up of three basic layers – Input, Hidden and Output. The input layer accepts the inputs, the hidden
              layer processes the inputs, and the output layer produces the result where each layer tries to learn from the
              computed weights. It is the foundation of AI and is used to solve complex problems that are difficult for humans.
              It consists of hardware or software that operates just like neurons of the human brain. Commercial application of
              ANN is in solving complex signal processing, predictions or pattern recognition problems.

              Convolutional Neural Networks (CNN)


              The Convolutional Neural Network is part of the Neural Networks that is primarily used for image related tasks. It
              extracts spatial features from data. It is used in Image classification (e.g., object detection), Medical imaging (e.g.,
              tumor detection), Facial recognition, Autonomous vehicles, etc.


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