Artificial Neural Network

  Machine learning project development: Design of ANN Artificial Neural Network(ANN) is the most suitable architecture in many of the AI applications. The back propagation Feed Forward Neural Network is popular in solving many non-linear multivariate and complex problems.   Design of ANN: Important steps in designing ANN algorithm is   1.     Collection of data   2.      Structure of ANN 3.      Training of Network 4.      Testing of Network 5.      Validation   of Neural Network 6.      Performance Analysis       1.Collection of data: To train the network strong database is required. There are many websites available that will provide requires data set for AI applications. Few   dataset finders are Google Dataset Search , Kaggle , UCI Machine Learning Repository , VisualData , CMU libraries Big Bad NLP...

  Machine Learning Project development Step 2: Choice of algorithm based on Supervised Learning Algorithm: First step2 : Machine learning project development: Choose an algorithm if the project is coming under supervised learning There are many supervised algorithms. A supervised learning algorithm is divided into regression and classification. If the output is discrete or binary choose the classification algorithm and if it is continuous choose regression. So step 2 is -- Identify whether the problem is coming under regression or classification?. Choose the algorithm accordingly. Different algorithms are used for regression and classification problems. A few of them are listed below. Algorithm for regression analysis 1.        Linear Regression 2.        Decision tree regression 3.        Random Forest 4.        Gradient boosted trees 5. ...

  Machine Learning Project development step1: Supervised or Unsupervised learning First step 1 : Machine learning project development: Identify whether the project is coming under supervised learning or unsupervised learning. Machine learning Machine learning is a part of artificial intelligence (AI). It provides Model   which learns from data (experience ) and predict, classify or group the data at the output. Applications of machine learning are as shown in figure1.                                                   Figure1:Applications of machine Learning Machine learning algorithms are often categorized as supervised or unsupervised and reinforcement. Therefore first step of machine learning   project is, identify whether the project   is coming under supervised learning or unsupervised learning. If you...

Training and Testing Phase of Neural Network: Training and Testing is an important part of Artificial Neural Network.Inborn baby's biological Neural Network is untrained and unable to recognize  environment.Parents slowly started to train the baby and baby will recognize the things.  Every neural network  it may be Natural or Artificial need to train the network and then test the network.If network is unable to do the task retrain with additional examples.Natural and Artificial neural network need to train . For every Neural Network following are the major steps to build the algorithm: 1. Collection of data          2. Training           3. Testing             4. Validation 5. Performance function