PyTorch’s Post

As a machine learning algorithm, a Backpropagation Neural Network (BPNN) can be trained to predict the noise levels a power transformer produces under different operating conditions and, in turn, utilized to reduce the unit’s noise pollution. With this training, a BPNN can identify transformers producing excessive noise and develop strategies to design ones that make less noise without sacrificing performance. This can be achieved by optimizing the design of the transformer core, windings, and other components. Its ability to control power transformer noise levels makes BPNN a promising technology in creating a more peaceful environment for everyone. #IntegratedEnergySolutions #EmpoweringBetterWorld

Achieving remarkable model accuracy with limited training resources can be a daunting challenge. Recently, I faced a scenario where I had to train a Single Layer Feedforward Neural Network (SLFN) model with few epochs. To overcome this constraint, I employed two powerful techniques: 1.L2 Regularization (Ridge Regression) Introduces a penalty term to the cost function, discouraging large weight values and reducing overfitting. This mitigated the risk of overfitting despite limited training iterations. 2.Batch Processing Divides training data into small batches and updates model weights after each batch. This approach accelerates the training process and acts as a form of regularization by introducing noise into the weight updates, further enhancing generalization. The combination of L2 regularization and batch processing proved highly effective. Despite the restricted number of epochs, the SLFN model achieved remarkable accuracy, demonstrating the power of these techniques in optimizing model performance under resource constraints.

No of Epoch  :  Epoch is one complete pass through the entire training dataset. The number of epochs is the number of times the learning algorithm will work through the entire training dataset. Learning rate : The learning rate determines the size of the step taken during the optimization process Batch size: Batch size is the number of training examples utilized in one iteration. It affects the speed and memory usage of the training process. Common choices include 32, 64, 128, and 256 Activation : Activation functions introduce non-linearity into the neural network. Common choices include ReLU (Rectified Linear Unit), Sigmoid, and Tanh. The choice of activation function can influence how well the network learns complex patterns. #artificialintelligence #neuralnetworks #deeplearning #neurons #machine #xevensolutions

Hi all, I have successfully completed the "Introduction to Artificial Intelligence" course offered by Great Learning Academy. #GreatLearningAcademy #greatlearning #glacertificate in this course I learned about computer vision and neural network ...