Learning AI from scratch– category –

Recap: The Evolution of Self-Supervised Learning In the previous lesson, we explored the latest advancements in self-supervised learning, including techniques like contrastive learning, masked autoencoders, BYOL, and CLIP. These methods ...

Recap of the Previous Lesson: Multi-Agent Reinforcement Learning In the last article, we covered Multi-Agent Reinforcement Learning (MARL), a method where multiple agents learn and interact within the same environment. These agents colla...

Recap: Anomaly Detection In the previous lesson, we explored Anomaly Detection, a technique used to identify data points or behaviors that deviate from normal patterns. Anomaly detection plays a crucial role in various industries such as...

Recap: Time Series Forecasting In the previous lesson, we explored Time Series Forecasting, a method that uses past data to predict future values. This technique is widely applied in various fields, such as stock price prediction, weathe...

Recap: Latest Trends in Deep Learning In the previous lesson, we discussed the latest research topics in the world of deep learning. These included self-supervised learning, Transformer models, large language models, multimodal AI, and t...

Recap of the Previous Lesson: The Details of the Self-Attention Mechanism In the previous lesson, we discussed the Self-Attention Mechanism, which is a core component of the Transformer model. This mechanism enables each word in a senten...

Recap: Zero-Shot Learning In the previous session, we explored Zero-Shot Learning (ZSL). ZSL enables a model to predict unseen classes that were not included in the training data, pushing the boundaries of traditional machine learning ap...

Recap: Meta-Learning In the previous session, we explored Meta-Learning, a method that enables models to quickly adapt to new tasks or datasets. Meta-Learning focuses on teaching models how to learn more efficiently, making them highly f...

Recap: Federated Learning In the previous session, we discussed Federated Learning, a method that allows distributed devices and servers to collaboratively train models without centralizing data. Each device processes its local data, sen...

Recap: Edge AI In the previous session, we covered Edge AI, a technology that enables AI models to run directly on devices, allowing for real-time data processing. Since data doesn’t need to be sent to the cloud, Edge AI reduces latency,...

Recap: Quantum Machine Learning In the previous session, we explored Quantum Machine Learning (QML), which leverages the power of quantum computers to solve problems that are challenging for traditional machine learning. QML is especiall...

Recap: Hardware Acceleration In the previous session, we explored Hardware Acceleration, focusing on how GPUs and TPUs are used to speed up machine learning model training and inference. These specialized hardware components are crucial ...

Recap: Model Compression In the previous lesson, we discussed Model Compression, a set of techniques like pruning, quantization, and knowledge distillation that help reduce the size and computational load of machine learning models. Thes...

Recap: Knowledge Distillation In the previous session, we explored Knowledge Distillation, a technique that transfers knowledge from a large teacher model to a smaller student model. This approach enables high-accuracy models to run in r...

Recap: SHAP and LIME In the previous lesson, we covered SHAP and LIME, two powerful techniques used to improve the interpretability of machine learning models. SHAP values quantify the contribution of each feature to a prediction, offeri...