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The random initialization, that generates weights are with same probability an is equivalent of random weight generation of Uniform distribution. A Uniform distribution, has a constant probability. It is also known as Rectangular Distribution. An alternative way to initialize the weights uniformly from the uniform distribution is the Uniform distribution. Each number has an equal probability of being selected in the uniform distribution. Choosing high values of weights is not the best for the model as it brings problems of bursting and vanishing gradients. Small random numbers, which are similar to 0, are the general way to initialize weights. Starting your weights in the range is ee ee ny ee, ce oe ee | rr a rr. rr.

Figure 3 The random initialization, that generates weights are with same probability an is equivalent of random weight generation of Uniform distribution. A Uniform distribution, has a constant probability. It is also known as Rectangular Distribution. An alternative way to initialize the weights uniformly from the uniform distribution is the Uniform distribution. Each number has an equal probability of being selected in the uniform distribution. Choosing high values of weights is not the best for the model as it brings problems of bursting and vanishing gradients. Small random numbers, which are similar to 0, are the general way to initialize weights. Starting your weights in the range is ee ee ny ee, ce oe ee | rr a rr. rr.

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Fig: 3.1 Structure of the Artificial Neural Networks It consists of three layers, the input layer, the hidden layer and the output layer as in fig 3.1. The input layer is the passive layer, which is responsible to take input and give to the next layer. The hidden layer is the middle layer between the input and the output layer. It is responsible for weight initialization of the neural network and performs the nonlinear transformation with the activation functions. The hidden layer is responsible for all the feature selection from the input layer. The output layer produces the final results after performing calculations from the previous layers
Fig: 3.1 Structure of the Artificial Neural Networks It consists of three layers, the input layer, the hidden layer and the output layer as in fig 3.1. The input layer is the passive layer, which is responsible to take input and give to the next layer. The hidden layer is the middle layer between the input and the output layer. It is responsible for weight initialization of the neural network and performs the nonlinear transformation with the activation functions. The hidden layer is responsible for all the feature selection from the input layer. The output layer produces the final results after performing calculations from the previous layers
Fig 3.2 Random Normal distribution The Random Normal weight initialization technique is also known as Gaussian distribution. It is a bell shape curve. The measurement in normal distribution has equal number of measurements below an above the mean value. The general formula for the probability density function of the normal distribution is,
Fig 3.2 Random Normal distribution The Random Normal weight initialization technique is also known as Gaussian distribution. It is a bell shape curve. The measurement in normal distribution has equal number of measurements below an above the mean value. The general formula for the probability density function of the normal distribution is,
Fig 4.1 Graph for various weight initialization techniques
Fig 4.1 Graph for various weight initialization techniques
Fig 4.3 Step Loss with different weight initialization Fig 4.2 Sample Graph for input v/s target function
Fig 4.3 Step Loss with different weight initialization Fig 4.2 Sample Graph for input v/s target function
Fig 4.4 Step Loss with different bias initialization
Fig 4.4 Step Loss with different bias initialization
Fig 4.5 Test Accuracy for different Learning rate
Fig 4.5 Test Accuracy for different Learning rate
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