To extend the CG-like-Adam algorithm to other deep learning tasks beyond image classification, such as natural language processing or reinforcement learning, several adaptations can be made. For natural language processing tasks like text generation or sentiment analysis, the CG-like-Adam algorithm can be applied to optimize recurrent neural networks (RNNs) or transformer models. By incorporating the conjugate-gradient-like approach into the optimization process of these models, improvements in training speed and convergence can be achieved. In reinforcement learning, CG-like-Adam can be utilized to optimize policy gradients in deep reinforcement learning algorithms. By integrating the conjugate-gradient-like direction into the policy optimization process, the algorithm can potentially enhance the learning efficiency and stability in reinforcement learning tasks. Additionally, exploring the application of CG-like-Adam in tasks with sparse rewards or complex action spaces could provide valuable insights into its effectiveness across a broader range of deep learning applications.