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Artificial Neural Network

  How training happens in ANN? Training in ML Algorithms: Consider Linear / Logistic Regression models where the training aimed at finding optimum values of coefficients / weights of the linear model (y =WX+B where W denotes the coefficient and C denotes the intercept of the line) Gradient Descent is one of the optimization technique for finding optimum weights of a model using iterative approach          W := W - Learning rate * derivative of Loss w.r.t W          B :=  B   - Learning rate * derivative of Loss w.r.t B W = coefficients of the linear model B = Intercept of the linear model Learning rate = Controllable parameter for getting optimum solution Loss function = Vector difference between predicted and actual outputs once we find optimum W,C value, we can able to perform the predictions in ML through newly trained model. In case of Neural networks, the above coefficients needs to be calculated for every node ...
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Deep Learning

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  Deep Learning is a part of Machine Learning where the learning / model training mimics human brain, this is possible because of the Artificial Network that resembles the human neural network which is thinking pattern of human brain. Deep Learning can be applied to both Structured and Unstructured data. Deep Learning applications are again classified to 3 categories same as Machine Learning Deep Supervised Learning Deep Unsupervised Learning Deep Reinforcement Learning Artificial Neural Networks (ANN): ANN is the basic structure for any model training through Deep Learning Any ANN model contains an input layer, hidden layer/s and an output layer, based on the problem statement, the complexity (nodes,hidden layers) of the model will be increased. Node: Similar to the Neuron of human brain, Node is a basic computational unit of the ANN. Activation Function decides the range of output of the node, below are the types of activation functions Sigmoid Tanh ReLU (Rectified Linear Unit)...
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Supervised Machine Learning

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 In Supervised Machine Learning output prediction will be done based on either mathematical or probabilistic approaches , based on output data type the supervised learning problems are classified in to two types Regression (Numerical data) Classification (Categorical data) Algorithms: below are the algorithms available for Supervised Machine Learning: Metrics: Metrics are measure of how good our model is performing during training and testing phases. below are the metrics available for Regression and Classification problems
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Machine Learning

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 Famous Definition which i like: A computer program / algorithm is said to be learning by itself if, the performance of doing a task increases with experience. Types of Machine Learning: 1.Basis on Data 2.Basis on solution approach / category Data: Data can be either in a structured form or in unstructured form Structured Data: Data that is stored in a structured manner, where data will be present in the form of columns/headers. this data can be in any of the artificial data formats mentioned below: CSV, TSV, XML, JSON, YAML,DBMS,HDFS etc... Unstructured Data: This type of files does not follow a column or header based structure for storing the data, below are some of the examples Audio, Video, Image,text  files etc... Classification of structured Data: 1. Basis on Data:                 Machine Learning techniques are applied on Structured data where as Deep Learning    techniques are applied over Unstructured data ...
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What is AI ?

 Artificial Intelligence is introduced to mimic the human intelligence for solving complex tasks humans encounter. Key tools in building AI system: 1. Brain - Experiencing or Learning something from the signals it received 2. Sense Organs - provides several type of inputs to the brains 3. Emotions below are some of the fields in which AI is already operational - Natural Language Processing products: These products are build to use speech or text or voice for getting meaningful insights (applications of NLP can be easily found over the internet) Data generators for NLP : Mouth - speech / audio (artificial equivalent) Ears - speech / audio sensors (artificial equivalent) Computer Vision Products: Computer Vision tools help in getting insights from images / videos (applications of CV can be easily found over the internet) Data generators for CV: Eyes - image / video (artificial equivalent) Structured Data Products: These products are helpful to gain insights / solve tasks easily from...
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