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I recently tackled a challenging research task involving multimodal data for a classification problem using [TensorFlow Keras](https://www.tensorflow.org/guide/keras). One of the trickiest aspects was figuring out how to load multimodal data in batches from storage efficiently. While TensorFlow Keras offers helpful functions for batch-loading images from various sources, the documentation and …

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In a [previous tutorial](https://www.daniweb.com/programming/computer-science/tutorials/541123/stock-price-prediction-using-1d-cnn-in-tensorflow-keras), I covered how to predict future stock prices using a deep learning model with 1D CNN layers. This method is effective for basic time series forecasting. Recently, I've enhanced this model by not just considering past closing prices but also factors like Open, High, Low, Volume, …

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Stock price prediction is a challenging task that requires analyzing historical trends, market sentiments, economic indicators, and company performance. One of the popular methods for stock price prediction is using deep learning models, such as convolutional neural networks (CNNs). CNNs are a type of neural network that can extract features …

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Chatbots are software applications that can interact with humans using natural language. They can be used for various purposes, such as customer service, entertainment, education, and more. Chatbots can be built using different techniques like rule-based systems, machine learning, or deep learning. In this article, I will focus on the …

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Language modeling is the cornerstone of advanced natural language processing, forming the backbone for cutting-edge technologies like ChatGPT. At its core, it involves predicting words based on context, a fundamental principle underlying modern large language Models (LLMs). There are various techniques for language modeling, with attention mechanisms emerging as the …

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The End.