Deep learning involves arriving at concrete memory and recall ability outcomes, which were not possible in surface learning
i.e. one who understands quantum mechanics knows by memory that in simple potential cases the wavefuntion is plane wave and in free particle sinusiodal or that operators with that commute have same eigenvalues and similar probabilities.
But these are not outcomes of surface knowledge and only reached at an advanced level of understanding where memorization is EASY
Thus memory's role in learning theories is unjustifiably minimized
Memory plays a crucial role in both surface learning and deep learning. While surface learning often involves rote memorization of facts or information without a deep understanding, deep learning goes beyond mere memorization to encompass a more profound comprehension and integration of knowledge.
In the context of deep learning, memory serves as a foundation upon which higher-level understanding and complex cognitive processes can be built. Deep learning involves making connections, identifying patterns, and developing a more holistic view of the subject matter. In this process, memory acts as a repository for the acquired knowledge, allowing individuals to recall and retrieve information effortlessly.
Deep learning enables a person to understand the underlying principles, relationships, and concepts, which in turn facilitates easier memorization. When someone truly grasps a subject, it becomes easier for them to remember and recall information related to that topic. The integrated understanding gained through deep learning allows for more efficient encoding, consolidation, and retrieval of information from memory.
It's important to note that deep learning encompasses not only memorization but also the ability to apply knowledge, analyze complex problems, think critically, and make connections between different concepts. While memory is a vital component of deep learning, it is not the sole determinant. Deep learning involves a deeper level of engagement with the material, critical thinking, and the ability to transfer knowledge to new contexts.
Overall, deep learning and memory are intertwined. Deep learning leads to a more profound understanding, which in turn enhances the ability to memorize and recall information. Memory, along with other cognitive processes, contributes to the development of deep learning capabilities.
Memory and deep learning are related but distinct concepts. Memory refers to the ability to store and retrieve information, while deep learning is a subfield of machine learning focused on training artificial neural networks. Deep learning can enhance memory processes by providing meaningful and context-rich learning experiences, but it does not directly mimic human memory. Deep learning algorithms excel in pattern recognition and prediction, which can aid tasks related to memory, but memory itself involves encoding, storage, and retrieval processes.
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