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Explore common myths in healthcare AI, discussing data needs, model accuracy, and clinical impact. Gain insights from experts on building effective AI solutions for medical applications.
Explore federated learning for privacy-preserving machine learning on decentralized data. Learn about its implementation in Google products and simulation using TensorFlow Federated.
Explore NLP fundamentals with TensorFlow, from tokenization to sentiment analysis and AI-generated poetry, in this beginner-friendly series led by Laurence Moroney.
Comprehensive series exploring TensorFlow's capabilities, from basics to advanced topics like NLP, computer vision, and recommendation systems. Hands-on coding tutorials for various ML applications.
Deep dive into TensorFlow's internal workings, covering advanced topics like parameter servers, quantization, debugging, and optimization, presented by the framework's developers.
Learn TensorFlow Extended (TFX) for production ML pipelines, covering workflows, metadata, distributed processing, model understanding, and continuous retraining for effective MLOps implementation.
Learn to create powerful recommendation systems using TensorFlow, covering content-based and collaborative filtering, ranking models, multitask learning, and efficient serving with ScaNN.
Explore AI's potential and challenges in various sectors, from facial recognition to medical devices. Learn about bias, career success prediction, and building better technology for societal benefit.
Explore TensorFlow Lite integration in Android using Google Play services, enabling efficient ML model deployment, reduced app size, and improved performance without static library bundling.
Explore Stable Diffusion for text-to-image generation using Keras. Learn to create custom images with Python, understand GANs, and dive into neural networks and convolutional computations.
Hands-on introduction to machine learning, covering models, projects, and tools. Learn to use CoLabs and leverage documentation for your first ML project.
Learn to create a custom machine learning model using TensorFlow.js for object recognition in the browser. Explore transfer learning techniques to build and train models quickly for various applications.
Learn to streamline model development with Keras Preprocessing Layers. Build end-to-end models, transform inputs, and improve performance using tf.data and multi-worker training.
Explore UX considerations for AI/ML products with Senior UX Designer Di Dang. Learn about user needs, mental models, explainability, and trust to enhance your machine learning product decisions.
Explore federated learning basics and its application to mobile keyboards, covering practical implementation and real-world use cases in this informative tech talk.
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