Obtained certification in Generative AI from Databricks, a leading platform for data engineering and machine learning. The certification program covered advanced techniques and applications of generative models, providing comprehensive knowledge and practical skills in leveraging AI algorithms for creative and innovative solutions.

Skills:

• Generative Adversarial Networks (GANs): Proficient in designing and implementing GAN architectures for generating realistic data samples, including images, text, and other structured data.

• Variational Autoencoders (VAEs): Experienced in building VAE models to learn latent representations of complex data distributions and generate new samples with controllable features.

• Deep Learning with TensorFlow and PyTorch: Skilled in utilizing deep learning frameworks such as TensorFlow and PyTorch to implement and train generative models effectively.

• Natural Language Processing (NLP): Familiarity with applying generative models in NLP tasks, including text generation, sentiment analysis, and language translation.

• Image Generation and Manipulation: Proficient in using generative models to create and manipulate images, including style transfer, image-to-image translation, and image super-resolution.

Time Series Generation: Knowledgeable in generating synthetic time series data using generative models for various applications, including financial forecasting, anomaly detection, and simulation.

• Model Evaluation and Optimization: Skilled in evaluating the performance of generative models using metrics such as inception score, Frechet Inception Distance (FID), and perceptual similarity. Proficient in optimizing model architectures and hyperparameters for improved performance.

• Deployment and Scalability: Understanding of deploying generative models in production environments using scalable infrastructure and cloud computing platforms, including Databricks.