Pinned Repositories
Deep-Learning-Specialization_Coursera
Welcome to the repository of my completed 'Deep Learning Specialization' course's assignments offered by DeepLearning.AI at Coursera.
Generative-Adversarial-Networks-GANs-Specialization
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more. The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
Jenkins
machine-learning-engineering-for-production-public
Public repo for DeepLearning.AI MLEP Specialization
ML-algorithm-from-scratch
Machine learning algorithms are mathematical model mapping methods used to learn or uncover underlying patterns embedded in the data. Machine learning comprises a group of computational algorithms that can perform pattern recognition, classification, and prediction on data by learning from existing data. To understand the inner works of an algorithm, it is very necessary to design them from scratch.
ml-data-pipeline
Spectrum-Based-Traffic-Clasification-in-Wireless-Network-using-Deep-Hybrid-Neural-Network
TensorFlow-Advanced-Techniques-Specialization
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. This course provides knowledge to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions
Time-series-analysis-by-Sequence-Modelling
Modeling multivariate time series has long been an attractive subject from a diverse range of fields including renewable energy, economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by sequence modelling.
rahmanhabib010's Repositories
rahmanhabib010/Generative-Adversarial-Networks-GANs-Specialization
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more. The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.
rahmanhabib010/ML-algorithm-from-scratch
Machine learning algorithms are mathematical model mapping methods used to learn or uncover underlying patterns embedded in the data. Machine learning comprises a group of computational algorithms that can perform pattern recognition, classification, and prediction on data by learning from existing data. To understand the inner works of an algorithm, it is very necessary to design them from scratch.
rahmanhabib010/Time-series-analysis-by-Sequence-Modelling
Modeling multivariate time series has long been an attractive subject from a diverse range of fields including renewable energy, economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. To obtain accurate prediction, it is crucial to model long-term dependency in time series data, which can be achieved to some good extent by sequence modelling.
rahmanhabib010/Deep-Learning-Specialization_Coursera
Welcome to the repository of my completed 'Deep Learning Specialization' course's assignments offered by DeepLearning.AI at Coursera.
rahmanhabib010/Jenkins
rahmanhabib010/machine-learning-engineering-for-production-public
Public repo for DeepLearning.AI MLEP Specialization
rahmanhabib010/ml-data-pipeline
rahmanhabib010/Spectrum-Based-Traffic-Clasification-in-Wireless-Network-using-Deep-Hybrid-Neural-Network
rahmanhabib010/TensorFlow-Advanced-Techniques-Specialization
TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. This course provides knowledge to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions