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.

My solutions for the Advanced Machine Learning Specialization on Coursera

Completed labs and projects from the Machine Learning Specialization by Andrew Ng (Coursera/DeepLearning.AI). Includes supervised, unsupervised, and advanced ML algorithms. Clean code, key insights, and organized folders for each course.

Advanced Machine Learning specialisation by HSE University

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. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs.

EdgeECAM50_USB is a 5 MP, colour, USB 2.0 camera from e-con Systems, a leading Embedded Product Design Services Company which specializes in the advanced camera solutions. This product comes with inbuilt machine learning (ML) inferencing capabilities and options to load any TensorFlow Lite micro models to the device.

This repository contains my assignment solutions from the Machine Learning Specialization by Andrew Ng on Coursera. It includes hands-on projects for supervised learning, advanced ML algorithms, unsupervised learning, recommender systems, and reinforcement

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.

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.

Advanced ML Specialization

Coursera course (in progress)

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HSE Advanced ML Specialization - Computer Vision Module

A hands-on journey through advanced machine learning topics including neural networks, backpropagation, softmax, and decision trees — with TensorFlow and scikit-learn.

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Projects of the MIT Data Science and ML Advanced Specialization

Advanced media organization system with ML and Italian music specialization

Repository for my run of the ML Specialization Advanced Learning Algorithms course.

Advanced ML Coursera Specialization assignments offered by National Research University Higher school of economics(updating)

This repository is specially created for my work in the Advanced ML specialization offered by Coursera.

The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. Courses included: Supervised ML, Advanced Learning Algs, Unsupervised ML.

TensorFlow: Advanced Techiniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.

Advanced Data Bases course material at Politecnico di Torino - "2nd level Specializing Master, AI, ML and Cloud Computing"

These are the coding assignments from the first part of the MLOps Specialization. It focuses on the python for beginners, to advanced deployment of ML workflows using CLI and APIs.

Passionate, self-taught developer specializing in algorithmic trading. I build trading bots using Python, PyTorch, and advanced ML (PPO, LSTM). Let's build your next profitable trading strategy.

This repository contains the practical implementations and theoretical recaps from the **Advanced Machine Learning** specialization. The course focuses on modern ML/DL principles, transitioning from theory to real-world applications in Computer Vision (CV) and Natural Language Understanding (NLU).

CS student in India specializing in AI & ML․ Researching the intersection of classical symbolic logic (Prolog) and modern statistical learning theories․ I'm a sucker for Advanced Math‚ physics & Fourier Transforms for building ML pipelines in Python․ When I'm not coding‚ I'm debating tech ethics or globalization․