Deep Learning

About the Course

Perceptrons, Neurons, Backpropagation, Multi-layer, Hyperparamaters, CNN and RNN

Course Contents

  •  Neural Network Architecture
  • In this chapter you will learn about the architectures of AI and where does Machine Learning and Deep Learning lie.
  •  Single Layer Perceptron
  • In this chapter the basic neuron architecture is discussed.
  •  Aggregation Function
  • Input to the neuron is achieved by aggregating data or input from the external world. The purpose of the aggregation function is combine the inputs and the weights.
  •  Activation Function
  • The aggregated input is fed into an activation function which give the output of the neuron. The type of problem decides the nature of the activation function.
  •  Implementing Boolean Logic
  • The neuron has been used to implement basic Boolean logic such as OR, NOT, NAND and AND gates. In this chapter we see how to implement this.
  •  Backward Propagation
  • In a multi-layer perceptron architecture the weights are updated using back propagation. In this chapter this method is dicussed.

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