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Explore natural language processing's evolution, focusing on Transformer architecture, language modeling, and BERT's impact on transfer learning in this comprehensive tech talk.
Explore Explainable AI, interpretable ML methods, and Integrated Gradients in this comprehensive talk. Gain insights into attribution baselines and future research directions.
Explore Generative Adversarial Networks (GANs) and TF-GAN library. Learn GAN applications, metrics, self-attention GAN, and practical implementation tips from a TensorFlow Research Engineer.
Comprehensive introduction to graph neural networks, covering theory, applications, and hands-on practice with a Colab exercise. Ideal for those interested in advanced machine learning techniques.
Explore key linear algebra concepts for machine learning, including data representations, vector embeddings, and dimensionality reduction, with insights from a postdoc at X.
Explore TensorFlow fundamentals: tensors, variables, gradients, modules, training loops, and tf.function. Ideal for those with ML basics seeking a deeper understanding of TensorFlow's core concepts.
Gain insights on building trustworthy AI products through exercises and resources from the People + AI Research team, focusing on user-centered design and calibrating trust in AI development.
Comprehensive introduction to deep learning concepts, neural network architectures, and practical applications, featuring TensorFlow examples, code demos, and insights into representation learning and model interpretability.
Explore parameter server training in TensorFlow for scaling model training across multiple machines, covering concepts, APIs, limitations, and future developments.
Explore machine learning in JavaScript with TensorFlow.js, from computer vision to creative prototypes. Learn to implement ML in browsers and beyond, unlocking new possibilities for your projects.
Explore research and engineering challenges in building machine learning infrastructure, focusing on TensorFlow runtime and optimization techniques for ML programs.
Learn quantization aware training in TensorFlow, covering fundamentals, APIs, and implementation. Optimize ML models with hands-on guidance from a Software Engineer.
Explore the new TF Lite Converter with Software Engineer Yu-Cheng Ling, covering its advantages, implementation, and transformation processes for efficient mobile deployment.
Explore TensorFlow debugging techniques for TF 2 and TF 1, covering tensor value printing, device placement, graph visualization, and step debugging in Keras models.
Explore cryogenic CMOS integrated circuits for controlling transmon qubits in quantum computing, focusing on scalable solutions for future fault-tolerant systems with millions of qubits.
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