GANs consist of three terms Generative Adversarial Network. Let's understand these three terms first. Generative : A Generative Model takes input training sample from some distribution and learns to represents that distribution. Adversarial : It basically means Conflicting or Opposing. Networks : These are basically neural networks. So,Generative Adversarial Networks are deep neural network architecture comprising of two neural networks compete with each other to make a generative model. A GAN consist of two class models : Discriminative Model :- It is the one that discriminate between two different classes of data.It tries to identify real data from fakes created by the generator Generative Model :- The Generator turns noise into an imitation of the data to try to trick the discriminator Mathematically, A Generative Model 'G' to be trained on training data 'X' sampled from some true distribution 'D' is the one which, given some standard random distrib
Machine Learning
It is basically a Subset of AI (Artificial Intelligence) which enables Machine to Learn from Data.
Artificial Intelligence is basically the domain of producing Intelligent Machines. All the machine which performs some smart activity can be called as an Artificially Intelligent Machine like an Air Conditioner changing the Temperature inside the room by sensing the Temperature from the outside. Such machines are not using any Machine Learning or Deep Learning Techniques.So such machine might come in the Artificial Intelligent set but not in the Machine Learning or Deep Learning part.
How does a Machine Learns from Data?
A Machine Learns from data using a Machine Learning Algorithm which take's the data as an input and predicts the output by finding patterns in the data.
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised Learning
In Supervised Learning, we teach the machine how to do something. The Datasets Provided for training is with labels ie. for any value of 'x' their is an output 'y'. The machine then generates an equation which can generate a value of 'y' for any value of 'x'. Using supervised learning Regression and classification problems are solved.
Unsupervised Learning
In Unsupervised Learning,the machine learns on its own. The Datasets is provided for training is without labels, and a
model learns useful properties of the structure of the Datasets. We do
not tell the model what it must learn, but allow it to find patterns and
draw conclusion from the unlabeled data. The algorithms in unsupervised
learning are more difficult than in supervised learning, since we have
little or no information about the data.
Reinforcement Learning
In Reinforcement Learning, the machine learns from trail and error methods. This kind of learning involves an agent that interacts with the environment to create action and then discover error or reward for that action.
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