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Comprehensive program teaching data engineering fundamentals, AWS tools, and real-world practices. Covers data lifecycle, ingestion, storage, modeling, and serving for analytics and machine learning applications.
Explore GANs for data augmentation, privacy, and image translation. Implement Pix2Pix and CycleGAN models to transform images, from satellite views to map routes and horses to zebras.
Streamline ETL tasks, optimize data pipelines, and publish datasets using TensorFlow Data Services. Learn to load, process, and share standardized data efficiently for machine learning models.
In this course, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports.
Explore advanced computer vision techniques using TensorFlow, including image classification, segmentation, object detection, and model interpretation methods for building powerful ML models.
Explore advanced deployment scenarios using TensorFlow Serving, TensorFlow Hub, TensorBoard, and federated learning to effectively bring machine learning models into real-world applications.
Explore the data engineering lifecycle, from data generation to serving, and build an end-to-end system. Gain practical skills in gathering requirements, choosing tools, and applying data architecture principles.
Explore advanced TensorFlow techniques for custom training loops, graph mode optimization, and distributed training on GPUs and TPUs. Gain flexibility and efficiency in model development.
Learn to automate complex workflows using ChatGPT API, building prompt chains, integrating Python code, and creating a customer service chatbot. Apply skills to real-world scenarios and enhance development capabilities.
Explore AI applications in disaster management through case studies on Hurricane Harvey and Haiti earthquake, using computer vision and NLP to analyze imagery and aid requests.
Обзор ИИ для нетехнических специалистов: понимание терминологии, возможностей, применения в бизнесе, работы с ИИ-командами и этических аспектов для эффективного внедрения в организации.
Explore advanced deep learning techniques: neural style transfer, autoencoders, VAEs, and GANs. Create unique images, denoise data, generate new content, and build powerful models using TensorFlow.
Learn to design and organize AI agent teams for complex tasks using crewAI. Master role-playing, memory, tools, focus, guardrails, and cooperation to automate business processes effectively.
AIリテラシーの基礎から実践まで学び、組織のAI活用を促進。AIの可能性、プロジェクト管理、倫理的課題を理解し、日本の産業におけるAI活用戦略を習得。
Expand LLM capabilities in application development using LangChain. Learn models, prompts, parsers, memories, chains, question answering, and agents to create robust AI applications efficiently.
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