Hesham Haroon

OpenDevin is the open source project build after the Devin first AI software tool, I worked on building the agents in the first stage

Bidirectional Encoder Representations from Transformers, better known as BERT, is a revolutionary paper by Google that increased the State-of-the-art performance for various NLP tasks and was the stepping stone for many other revolutionary architectures.
It's not an exaggeration to say that BERT set a new direction for the entire domain. It shows clear benefits of using pre-trained models (trained on huge datasets) and transfer learning independent of the downstream tasks.
his is a guided… عرض المزيد

Bidirectional Encoder Representations from Transformers, better known as BERT, is a revolutionary paper by Google that increased the State-of-the-art performance for various NLP tasks and was the stepping stone for many other revolutionary architectures.
It's not an exaggeration to say that BERT set a new direction for the entire domain. It shows clear benefits of using pre-trained models (trained on huge datasets) and transfer learning independent of the downstream tasks.
his is a guided project on fine-tuning a Bidirectional Transformers for Language Understanding (BERT) model for text classification with TensorFlow. In this 2.5 hour long project, you will learn to preprocess and tokenize data for BERT classification, build TensorFlow input pipelines for text data with the tf.data API, and train and evaluate a fine-tuned BERT model for text classification with TensorFlow 2 and TensorFlow Hub. عرض أقل

FastText has also published a fast and accurate tool for text-based language identification capable of reognizing more than 170 languages. The tool has been open-sourced to be used for free by anyone. To versions of language identification are available, each optimized for different memory usage.

lid.176.bin, which is faster and slightly more accurate, but has a file size of 126MB.
lid.176.ftz, which is the compressed version of the model, with a file size of 917kB. The smaller file… عرض المزيد

FastText has also published a fast and accurate tool for text-based language identification capable of reognizing more than 170 languages. The tool has been open-sourced to be used for free by anyone. To versions of language identification are available, each optimized for different memory usage.

lid.176.bin, which is faster and slightly more accurate, but has a file size of 126MB.
lid.176.ftz, which is the compressed version of the model, with a file size of 917kB. The smaller file size is achieved by a little compromise on accuracy عرض أقل

A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre-processing required in a ConvNet is much lower as compared to other classification algorithms. While in primitive methods filters are hand-engineered, with enough training, ConvNets have the ability to learn these filters/characteristics

TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.

IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.

The TfidfVectorizer converts a collection of raw… عرض المزيد

TF (Term Frequency): The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.

IDF (Inverse Document Frequency): Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.

The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.

Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector. عرض أقل

The Netflix Movies and Guest Stars in The Office project is a data analysis project that explores two different datasets. The first dataset is a collection of Netflix movies that includes information such as movie titles, countries of production, release years, and movie durations. The second dataset is a collection of episodes from the popular TV show "The Office" that includes information such as episode titles, air dates, and guest stars.

The main objective of this project is to… عرض المزيد

The Netflix Movies and Guest Stars in The Office project is a data analysis project that explores two different datasets. The first dataset is a collection of Netflix movies that includes information such as movie titles, countries of production, release years, and movie durations. The second dataset is a collection of episodes from the popular TV show "The Office" that includes information such as episode titles, air dates, and guest stars.

The main objective of this project is to practice data wrangling, visualization, and exploratory data analysis using Python and its powerful data science libraries such as Pandas, Matplotlib, and Seaborn. Throughout the project, we clean and manipulate the datasets to prepare them for analysis, explore relationships between variables using scatter plots, and use statistical methods to draw conclusions about trends and patterns in the data.

The project involves a series of guided steps that lead to the completion of a final analysis, which in this case is answering the question of whether Netflix movies are getting shorter over time. In addition to this analysis, we also explore the relationship between guest stars and ratings in "The Office" dataset. عرض أقل

This project is a Streamlit app for detecting tables in images, cropping them, detecting cells within the cropped tables, and applying OCR (Optical Character Recognition) to extract the table data into a CSV file.

build a multi-headed model that’s capable of detecting different types of of toxicity like threats, obscenity, insults, and identity-based hate better than Perspective’s current models. You’ll be using a dataset of comments from Wikipedia’s talk page edits. Improvements to the current model will hopefully help online discussion become more productive and respectful.