Journal of Information Technology & Software Engineering
Open Access

Challenges and Advancements of Artificial Neural Networks Training in Optimization with AutoML

Kevin Edward^{* *}Correspondence: Kevin Edward, Department of Software Technology, Royal Melbourne Institute of Technology, Melbourne, Australia, Email:

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Description

Neural networks, a type of artificial intelligence, teach computers to process information similarly to the human brain. Deep learning is a kind of machine learning technique that uses networked nodes or neurons arranged in a layered pattern to simulate the organization of the human brain. Artificial Neural Networks (ANNs) have transformed a number of fields, including robotics, healthcare, natural language processing, and computer vision. These networks provides increased accuracy and capacity as they get bigger and more complicated, but they also present significant problems in terms of interpretability, computational resources, and training efficiency. The high processing power needed for large-scale neural network training is one of the main obstacles. During training, large-scale computations are required for both forward and backward propagation in deep systems with millions of parameters. The intricacy of the network architecture and the extent of the dataset increase this need. Another significant difficulty is managing big datasets and model parameters with constrained memory resources. Although methods such as gradient checkpointing and memory-efficient optimization algorithms (like Adam and RMSprop) have been developed to lessen these limitations, they may not be adequate for very large models in many cases.

The risk of overfitting, a situation in which a model performs well on training data but is unable to generalize to new data, increases with the size of the model. This problem is partially mitigated by regularization strategies like weight decay and dropout, but finding the ideal trade-off between generalization and model complexity is still difficult. It is common for largescale models to require weeks or even longer for training. This longer duration limits the scalability of training on common hardware setups by delaying experimentation and raising computational costs. Scaling neural network training may encounter difficulties due to communication cost caused by distributed training across several Graphics Processing Unit (GPU) or even multiple computers. Reducing this overhead requires effective communication protocols and parallelization techniques. Scaling over several GPUs and computers is made possible by utilizing distributed training frameworks like TensorFlow and PyTorch Distributed. Methods like as data parallelism and model parallelism efficiently divide up the computations, cutting down on memory consumption and training time. For deep learning applications, specialized hardware such as GPUs and TPUs (Tensor Processing Units) speed up computations. New developments in hardware architecture, such tensor cores and mixed-precision training, greatly increase training speed and energy efficiency. It is now common practice to pre-train big models using methods like transfer learning (e.g., BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-Trained Transformers)) on enormous datasets (e.g., ImageNet, Wikipedia).

The amount of data and compute required for task-specific training is decreased when these pre-trained models are refined on particular tasks. The construction and optimization of neural network designs, hyperparameters, and training pipelines are automated using autoML (Automated Machine Learning) approaches. They make AI more approachable and effective by streamlining the process of developing and implementing largescale models. Understanding the decision-making procedures used by larger models is essential to transparency and trust. A new development in interpretability methods, such as saliency maps and attention mechanisms, helps in the dissection of intricate neural networks. Large-scale neural network training presents both opportunities and challenges that have a significant impact on many different industries. Large-scale models with higher accuracy and efficiency are useful for applications in finance, driverless cars, healthcare diagnostics, and other fields. The potential of machine learning is increased by technological developments in training techniques and hardware, which also drive breakthroughs in Artificial Intelligence (AI) research. It is imperative that concerns about biases, equality, and privacy be addressed as large-scale AI systems are implemented more frequently.

The training of large-scale neural networks has significant issues with interpretability, efficiency, and computational resources. On the other hand, innovative approaches to hardware acceleration, automated methods, and distributed computing present fresh chances to enhance AI capabilities. The development of large-scale neural networks contains the potential to transform industries and create more advancement in artificial intelligence, provided that academics and practitioners persist in addressing these obstacles. Without explicit function specifications, ANNs are capable of selfregulating data. They are efficient at classifying patterns and have the ability to compute in parallel. They do separate with the necessity of lengthy numerical and analytical computations.