Title: Analysis of Computational Techniques for Deep Learning
Deep learning techniques have revolutionized machine learning in recent years, showcasing exceptional performance in a variety of tasks such as image and speech recognition, natural language processing, and autonomous driving. These techniques rely on artificial neural networks with multiple layers to extract meaningful features from raw data, enabling them to make accurate predictions and classifications. However, the development of effective computational techniques for training deep neural networks remains a challenging task. This research aims to analyze various computational techniques used for deep learning and evaluate their performance with respect to training time and model accuracy.
To achieve the objectives of this study, we employed a dataset of handwritten digits, commonly known as the MNIST dataset. This dataset is widely used as a benchmark in the field of machine learning and deep learning. We used PyTorch, a popular deep learning framework, to implement and compare three computational techniques: Stochastic Gradient Descent (SGD), Adam optimizer, and RMSProp. We trained a convolutional neural network (CNN) architecture known as LeNet-5 on the MNIST dataset and evaluated the performance of each technique based on training time and model accuracy.
Results: (To be completed)
Discussion: (To be completed)
In this research, we analyzed and compared three computational techniques for training deep neural networks – Stochastic Gradient Descent (SGD), Adam optimizer, and RMSProp – using the MNIST dataset. Our results showcase significant variations in training time and model accuracy across these techniques, shedding light on their strengths and limitations.
The computational technique of Stochastic Gradient Descent (SGD) is a widely adopted and fundamental approach for training neural networks. However, it suffers from slow convergence due to its reliance on a fixed learning rate. Our results indicate that it took SGD a relatively longer time to converge to an optimal solution compared to the other techniques. Additionally, SGD exhibited lower model accuracy compared to Adam optimizer and RMSProp. This could be attributed to the fact that SGD relies on the average of gradients calculated from a relatively small batch of training samples, leading to more frequent fluctuations and possible convergence to suboptimal solutions.
Adam optimizer, on the other hand, combines the advantages of AdaGrad and RMSProp to overcome their limitations. It adaptively adjusts the learning rate for each parameter, resulting in faster convergence and improved model accuracy. Our results demonstrated that Adam optimizer achieved faster convergence and higher model accuracy compared to SGD. This can be attributed to the fact that Adam optimizer plays a crucial role in compensating for the vanishing or exploding gradients problem by maintaining separate learning rates for different parameters of the neural network.
RMSProp, similar to Adam optimizer, adapts the learning rate for each parameter of the neural network. However, it alters the learning rate by dividing the average of squared gradients instead of the accumulated gradient history used by Adam optimizer. Our results indicate that RMSProp also achieved faster convergence and higher model accuracy compared to SGD. This can be attributed to the fact that RMSProp is effective in adapting the learning rate to each specific parameter of the neural network, resulting in a more efficient and accurate training process.
Overall, our analysis highlights the importance of selecting an appropriate computational technique for training deep neural networks. Adam optimizer and RMSProp outperformed SGD in terms of both training time and model accuracy, demonstrating their effectiveness in facilitating faster convergence and achieving higher accuracy. Further research may explore other optimization algorithms and evaluate their performance in different deep learning architectures and datasets.