Developing cloud-based scalable web platforms using the cutting edge technologies of artificial intelligence, blockchain, and quantum computing.
Delivering high-impact digital products to the end-users and the enterprise.
Machine Learning Engineer, 10+ years of experience, Java and Python
Backend Engineer, 10+ years of experience, Java and PHP.
We are ready to help you with data analytics, big data, and ETL/ELT pipelines.
We will help you deploy scalable Elasticsearch clusters including Kibana insightful visualizations and dashboard. Ingest data with logstash pipelines using enrich transformations to process the data on the fly. We will help you with the cluster maintenance and setup alerts and notifications for cluster warning status.
# Apache Spark
We will help you develop distributed in-memory data processing using Apache Spark at scale. We develop, deploy, and maintain the Spark cluster using technologies such as Kubernetes, Spark Streaming and Spark ML.
# Apache Kafka
We leverage big data pipelines using Kafka. We make use of KSQL to summarize data on the fly in the queues before it is ingested into either Elasticsearch or another NoSQL data repository. We connect Kafka queues to Spark Streaming for further processing and transformations.
# AWS Cloud Architecture
We have rich experience in AWS cloud solutions architecture. We cover your needs when it comes to AWS EC2, AWS S3, AWS DynamoDB, AWS Lambda, AWS Kinesis, and many more AWS cloud services.
- Deep Reinforcement Learning Automated Trading Agent
- Sequence to Sequence Recurrent Networks for Time Series Forecasting
- Portfolio Optimization and Visualization Real-Time Dashboard
- Deep Neural Network Power Consumption Forecasting
- Big Data Acquisition and Transformation Pipeline
- Deployment with Cloud-Native Microservices and Asynchronous Messaging Queue
- Deep AutoEncoder User-Item Implicit Collaborashowtive Filtering
- Big Data Acquisition and Transformation Pipeline
- Admin Dashboard Integration and Customer Segmentation Visualization
- Optical Character Recognition OCR for Identity Card, Driving License, and Passport
- Face Detection, Recognition, and Matching Technologies.
- Deployment with Cloud-Native Microservices and Quantitative Performance Monitoring
- Development of E-Commerce Platform and Online Payment Gateway Integration
- Big Data Extraction, Aggregation and Transformation Pipeline
- Single Page Application Frontend
- Deep Convolutional Network for Product Detection and Recognition
- Real-Time Model Training on New Images
- RESTful APIs Deployment with Model Training Admin Dashboard Development
- Deep Learning Object Detection and Recognition
- Deep Learning Time Series Analysis and Forecasting
- Video Aggregation and Processing Cloud Pipeline
Introducing the study of machine learning and algorithmic trading for financial practitioners
Time series modeling and forecasting are tricky and challenging. The i.i.d ( identically distributed independence ) assumption does not hold well to time series data.
The question has been puzzling me for quite a while; How to become an expert in Computer Vision? And by an expert, I mean as a software engineer aka developer.
Quantum Computing & Quantum Information is the promising future of Computer Science. Quantum Computing is making a disruption in areas such as Cryptography.
Computer algorithms are inherent in every aspect of our daily lives without us noticing their existence. The smartphone in one’s pocket is thousands of times far more.
The self-driving car industry is now one of the very hottest trends, causing a loud buzz not only in academia and industry but also from a socio-economic perspective.
In this report, I shall summarize the objective functions ( loss functions ) most commonly used in Machine Learning & Deep Learning.
Water is one of our most precious natural resources, yet its availability has been severely impacted by the industrial revolution and resulting climate changes.
GraphGANs are constituted of a Generator that is learning the underlying connectivity distribution between vertices as P(V|v_c) and a Discriminator
Markovian Generative Adversarial Networks (MGANs) capture the feature statistics of Markovian Patches and generate images of arbitrary dimensions.
Combining Reinforcement Learning with Generative Adversarial Networks has been applied to many problems such as drug discovery and text generation.
MaskGAN allows the end-user to edit a segmentation mask which is used as a condition to apply face style manipulations interactively.
Once we start believing that we understand our history, a new breakthrough reveals unexpected secrets. Taking historical facts for granted is no longer our only choice.
Design and analysis cost functions for training deep learning models.
Perform data engineering on bank marketing dataset using Python, Pandas, and MatplotLib.
Implement Convolutional Neural Networks (CNN) as per LeNet5 for Handwritten digits recognition.
Implement a deep learning classifier on Kaggle using Python.
Implement and evaluate different regression analysis techniques using Python and SciKit-Learn.
I introduce a structured learning plan that could help you acquire the necessary qualifications with supplementary materials.
Demonstrate different IoT communication protocols such as MQTT and CoAP using Arduino, ESP8266, and Rasperry Pi Gateway.
Simplified mathematical derivation of Support Vector Machines (SVM) classifiers and different kernel functions.
Expose PyTorch model training and evaluation via RESTful APIs.
A talk about drug discovery techniques using deep learning, reinforcement learning, and generative adversarial networks (GANs).
Cairo 11513, Egypt.
+2 010 07 83 34 87