Article

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 ...

Recap of the Previous Lesson: Autoencoders In the previous lesson, we covered Autoencoders, a technique used for compressing and reconstructing data. Autoencoders compress input data into a low-dimensional latent representation and then ...

Recap of the Previous Lesson: Generative Adversarial Networks (GAN) In the previous article, we discussed Generative Adversarial Networks (GAN), a type of generative model where two networks, the generator and the discriminator, compete ...

Recap of the Previous Lesson: Self-Supervised Learning In the last lesson, we covered Self-Supervised Learning, a method that allows models to efficiently learn from unlabeled data by creating tasks such as predicting hidden portions of ...

Recap of the Previous Lesson: Overview of the GPT Model In the previous lesson, we discussed the GPT (Generative Pre-trained Transformer) model, which is specialized in natural language generation. GPT uses an autoregressive approach to ...

Recap of the Previous Lesson: The BERT Model In the previous lesson, we discussed BERT (Bidirectional Encoder Representations from Transformers), a Transformer-based model that captures bidirectional context, allowing it to deeply unders...

Recap of the Previous Lesson: The Transformer Model In the previous lesson, we explored the Transformer model, which has become the dominant architecture in natural language processing (NLP). Unlike traditional models such as RNNs or LST...

Recap of the Previous Lesson: The Attention Mechanism In the previous lesson, we discussed the Attention Mechanism, a technique that allows models to focus on the most important parts of input data. By focusing on key elements, the Atten...