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Purposive Learning01:22

Purposive Learning

174
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
174
Cognitive Learning01:21

Cognitive Learning

461
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
461
Oscillations In An LC Circuit01:30

Oscillations In An LC Circuit

2.4K
An idealized LC circuit of zero resistance can oscillate without any source of emf by shifting the energy stored in the circuit between the electric and magnetic fields. In such an LC circuit, if the capacitor contains a charge q before the switch is closed, then all the energy of the circuit is initially stored in the electric field of the capacitor. This energy is given by
2.4K
Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

2.8K
A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
2.8K
Introduction to Learning01:18

Introduction to Learning

492
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
492
Motivational Cycle01:20

Motivational Cycle

674
The motivational cycle is a key concept that explains how individuals are motivated to meet their needs. At its core, the cycle revolves around four distinct stages: need, drive, goal-directed behavior, and goal achievement. These stages respond to imbalances in the body or mind, prompting actions that restore balance.
The cycle begins with a need. This need can arise from various conditions, such as hunger, thirst, or temperature changes. For instance, when an individual feels cold, their body...
674

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Setup and Execution of the Rapid Cycle Deliberate Practice Death Notification Curriculum
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Cyclical Curriculum Learning.

H Toprak Kesgin, M Fatih Amasyali

    IEEE Transactions on Neural Networks and Learning Systems
    |April 17, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Cyclical curriculum learning (CCL) improves artificial neural network (ANN) training by cyclically altering dataset size. This novel approach outperforms standard and existing curriculum methods across diverse tasks.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Artificial neural networks (ANNs) mimic human learning but lack structured curricula.
    • Classical ANNs often use a 'vanilla' training method without optimized data sequencing.
    • Existing curriculum learning (CL) methods improve training but lack a universally efficient approach.

    Purpose of the Study:

    • To introduce a novel curriculum learning strategy called cyclical CL (CCL).
    • To enhance ANN training efficiency and performance across various datasets and architectures.
    • To provide a theoretically sound and empirically validated alternative to existing training methods.

    Main Methods:

    • Proposed Cyclical Curriculum Learning (CCL) where training data size is varied cyclically.
    • Compared CCL against vanilla training and existing CL methods.
    • Empirically validated CCL on 18 datasets and 15 architectures for image and text classification.
    • Provided theoretical analysis on the benefits of cyclical CL and vanilla method integration.

    Main Results:

    • CCL achieved superior results compared to no-CL and existing CL methods.
    • The proposed method demonstrated effectiveness across diverse image and text classification tasks.
    • Theoretical analysis supported the efficacy of cyclically combining CL and vanilla training.

    Conclusions:

    • Cyclical CL offers a more successful and robust training paradigm for ANNs.
    • Integrating vanilla and curriculum learning cyclically is theoretically and practically advantageous.
    • CCL presents a promising direction for improving deep learning model training.