This study provides Urdu poetry generated using different deep-learning techniques and algorithms. The data was collected through the Rekhta website, containing 1341 text files with several couplets. The data on poetry was not from any specific genre or poet. Instead, it was a collection of mixed Urdu poems and Ghazals. Different deep learning techniques, such as the model applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU), have been used. Natural Language Processing (NLP) may be used in machine learning to understand, analyze, and generate a language humans may use and understand. Much work has been done on generating poetry for different languages using different techniques. The collection and use of data were also different for different researchers. The primary purpose of this project is to provide a model that generates Urdu poems by using data completely, not by sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as in the base paper. The results have shown good accuracy in the poems generated by the model.

LSTM networks were designed specifically to overcome the long-term dependency problem faced by recurrent neural networks RNNs (due to the vanishing gradient problem). LSTMs have feedback connections which make them different to more traditional feedforward neural networks. This property enables LSTMs to process entire sequences of data (e.g. As a result, LSTMs are particularly good at processing sequences of data such as text, speech and general time-series. An example -- Consider we are trying to predict monthly ice cream sales.

In this article, we will utilize Tensorflow 2.0 and Python to create an end-to-end process for classifying movie reviews. Most Tensorflow tutorials focus on how to design and train a model using a preprocessed dataset. Typically preprocessing the data is the most time-consuming part of an AI project. This article will walk you through this process. Note: we are not trying to generate a state of the art classification model here.

This paper introduces a new perspective of intelligent robots and systems control. The presented and proposed cognitive model: Memory, Learning and Recognition (MLR), is an effort to bridge the gap between Robotics, AI, Cognitive Science, and Neuroscience. The currently existing gap prevents us from integrating the current advancement and achievements of these four research fields which are actively trying to define intelligence in either application-based way or in generic way. This cognitive model defines intelligence more specifically, parametrically and detailed. The proposed MLR model helps us create a general control model for robots and systems independent of their application domains and platforms since it is mainly based on the dataset provided for robots and systems controls. This paper is mainly proposing and introducing this concept and trying to prove this concept in a small scale, firstly through experimentation. The proposed concept is also applicable to other different platforms in real-time as well as in simulation.

We frequently hear about Machine Learning in the media, especially since the recent wave of interest in deep-learning. The perpetual improvement of Machine Learning techniques combined with the ever increasing amount of data that are stored suggests endless new applications. Many innovative solutions emerge: autonomous driving, next generation supermarkets with implicit payment, next generation chatbots that can interact with you as human beings would do, and so on. More than ever, the future seems within reach. But the more extravagant and original the application is, the more the layman is put off.

We frequently hear about Machine Learning in the media, especially since the recent wave of interest in deep-learning. The perpetual improvement of Machine Learning techniques combined with the ever increasing amount of data that are stored suggests endless new applications. Many innovative solutions emerge: autonomous driving, next generation supermarkets with implicit payment, next generation chatbots that can interact with you as human beings would do, and so on. More than ever, the future seems within reach. But the more extravagant and original the application is, the more the layman is put off.