ディープラーニングの応用と詳細（91～120）– ディープラーニングの具体的な応用例と高度な概念を学びます。 –

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...

Recap: Model Interpretability In the previous lesson, we discussed Model Interpretability, focusing on the importance of understanding which features influence the predictions of complex models, such as deep learning models. Interpretabi...

Recap of the Previous Lesson: Policy Gradient Methods In the previous lesson, we discussed Policy Gradient Methods, which directly optimize the policy (a strategy for choosing actions) in reinforcement learning. This approach is especial...

Recap of the Previous Lesson: Deep Q-Network (DQN) In the last article, we discussed Deep Q-Networks (DQN), a method that combines Q-learning with deep learning for reinforcement learning. DQN effectively learns how to select actions in ...

Recap of the Previous Lesson: Applications of Reinforcement Learning In the previous lesson, we discussed the applications of Reinforcement Learning (RL). We explored how RL is utilized in real-world scenarios, such as game AI, robotics,...

Recap of the Previous Lesson: Text-to-Speech (TTS) In the previous lesson, we covered Text-to-Speech (TTS), a technology that converts written text into real-time audio. TTS is widely used in applications such as smart speakers, car navi...

Recap of the Previous Lesson: Machine Translation Models In the previous article, we discussed machine translation models, particularly focusing on how neural machine translation (NMT) uses neural networks to produce high-quality transla...

Recap of the Previous Lesson: Text Generation with RNNs In the previous lesson, we discussed text generation using RNNs (Recurrent Neural Networks), which excel at predicting the next step while retaining past information. RNNs are widel...

Recap of the Previous Lesson: SSD Model In the previous article, we discussed the SSD (Single Shot MultiBox Detector) model, which, like YOLO, performs object detection and classification in a single inference. SSD excels at detecting sm...

Recap of the Previous Lesson: The YOLO Model In the previous lesson, we discussed the YOLO (You Only Look Once) model, a fast object detection method that processes the entire image at once to detect the position and type of objects simu...

Recap of the Previous Lesson: Segmentation In the previous article, we discussed segmentation, a technique that classifies every pixel in an image to determine which object or category it belongs to. Segmentation is widely used in fields...

Recap of the Previous Lesson: Object Detection In the previous article, we covered the basics of Object Detection, a technique that identifies objects within an image and determines their location by drawing bounding boxes around them. O...

Recap of the Previous Lesson: Image Classification with CNNs In the previous article, we explored the basic workings and methods of image classification using CNNs (Convolutional Neural Networks). CNNs are powerful tools that extract fea...

Recap of the Previous Lesson: Chapter 3 Summary and Comprehension Check In the previous article, we reviewed the techniques covered so far, including generative models and autoencoders. We revisited the core concepts of data compression ...