Bangla or Bengali is the national language of Bangladesh, people from different regions don't talk in proper Bangla. Every division of Bangladesh has its own local language like Sylheti, Chittagong etc. In recent years some papers were published on Bangla language like sentiment analysis, fake news detection and classifications, but a few of them were on Bangla languages. This research is for the local language and this particular paper is on Sylheti language. It presented a comprehensive system using Natural Language Processing or NLP techniques for translating Pure or Modern Bangla to locally spoken Sylheti Bangla language. Total 1200 data used for training 3 models LSTM, Bi-LSTM and Seq2Seq and LSTM scored the best in performance with 89.3% accuracy. The findings of this research may contribute to the growth of Bangla NLP researchers for future more advanced innovations.

Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball's location and improving tracking performance. Our approach leverages frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNet, as TrackNetV4.

In this paper, we introduce Dreamweaver, which belongs to a new class of auto-regressive decision-making models known as large reasoning models (LRMs). Dreamweaver is designed to improve 3D floorplanning in electronic design automation (EDA) via an architecture that melds advancements in sequence-to-sequence reinforcement learning algorithms. A significant advantage of our approach is its ability to effectively reason over large discrete action spaces, which is essential for handling the numerous potential positions for various functional blocks in floorplanning. Additionally, Dreamweaver demonstrates strong performance even when trained on entirely random trajectories, showcasing its capacity to leverage sub-optimal or non-expert trajectories to enhance its results. This innovative approach contributes to streamlining the integrated circuit (IC) design flow and reducing the high computational costs typically associated with floorplanning. We evaluate its performance against a current state-of-the-art method, highlighting notable improvements.

Unfortunately, the best policy from a set of many policies such estimates are often inaccurate (Fu et al., 2021). As given only logged data, is crucial for applying an alternative, many works have explored using empirical offline RL in real-world settings. One idea that Bellman errors to perform OMS, but have found them to has been extensively explored is to select policies be poor predictors of value model accuracy (Irpan et al., based on the mean squared Bellman error 2019; Paine et al., 2020). This has led to a belief among (MSBE) of the associated Q-functions. However, many researchers that Bellman errors are not useful for previous work has struggled to obtain adequate OMS (Géron, 2019; Fujimoto et al., 2022). OMS performance with Bellman errors, leading many researchers to abandon the idea. To this end, To this end, we propose a new algorithm, Supervised Bellman we elucidate why previous work has seen pessimistic Validation (SBV), that provides a better proxy for the results with Bellman errors and identify true Bellman errors than empirical Bellman errors. SBV conditions under which OMS algorithms based achieves strong performance on diverse tasks ranging from on Bellman errors will perform well. Moreover, healthcare problems (Klasnja et al., 2015) to Atari games we develop a new estimator of the MSBE that is (Bellemare et al., 2013).

The Convolutional Neural Network (CNN) is a multi-layered neural network that is known to be able to detect patterns and complex features. It has been useful in face detection, self-driving cars, and…

Neural Architecture Search (NAS) algorithms automate the task of finding optimal deep learning architectures given an initial search space of possible operations. Developing these search spaces is usually a manual affair with pre-optimized search spaces being more efficient, rather than searching from scratch. In this paper we present a new framework called Neural Architecture Type System (NeuralArTS) that categorizes the infinite set of network operations in a structured type system. We further demonstrate how NeuralArTS can be applied to convolutional layers and propose several future directions.

The Hyperspectral data expands the capability of Image Classification. The Hyperspectral Data not only distinguishes different land cover types but it also provides the detailed characteristics of each land cover such as minerals, soil, man-made structures (buildings, roads, etc.) and vegetation types. While dealing with the HyperSpectral data one disadvantage is that there are too many bands to process. Apart from that, it is a challenge to store such a large amount of data. With a large amount of data, the time complexity also increases.

For most of us, who were once newbies in Deep Learning, trying tf.keras.layers.Conv2D for MNIST classification was fun. Convolutions are the building blocks of most algorithms in computer vision, except for some newer variants like Vision Transformers, Mixers etc. which claim to solve image-related problems without the use of convolutions. At the core of DL, lies Gradient Descent ( and its variants), which help us optimize the parameters of a NN, which in turn reduces the loss we incur while training the model. Convolutions or Convolutional layers also possess their own parameters commonly known as filters. No, not filters but they are kernels, right?

For most of us, who were once newbies in Deep Learning, trying tf.keras.layers.Conv2D for MNIST classification was fun. Convolutions are the building blocks of most algorithms in computer vision, except for some newer variants like Vision Transformers, Mixers etc. which claim to solve image-related problems without the use of convolutions. At the core of DL, lies Gradient Descent ( and its variants), which help us optimize the parameters of a NN, which in turn reduces the loss we incur while training the model. Convolutions or Convolutional layers also possess their own parameters commonly known as filters. No, not filters but they are kernels, right?

In this article, we will try to implement the basic CNN model with the Keras framework. The benefit of the convolutional neural network is that it reduces or minimizes the dimension and parameters of images by retaining maximum information so that the training process becomes fast and takes less computation power. We will try to implement the code in google colab with a step-by-step process. Why we are using CNN? The main concern of using the convolutional neural network is for the images that previous algorithms are not so much suitable for bulk images dataset and retaining the image information.