Inside TensorFlow - tf.Keras - Part 1
Explore tf.Keras architecture, layers, and models with creator Francois Chollet. Learn about Functional API, eager execution, and automatic masking in this technical deep dive.
Inside TensorFlow - tf.distribute.Strategy
Explore tf.distribute.Strategy for efficient distributed TensorFlow training. Learn implementation details, data parallelism, and optimization techniques from a TensorFlow engineer.
Inside TensorFlow - Functions, Not Sessions
Deep dive into TensorFlow's evolution from tf.Session to tf.function, exploring internal architecture, graph generation, and key concepts for advanced TensorFlow development.
Inside TensorFlow - Resources and Variants
Deep dive into TensorFlow's internal architecture, exploring state management, dynamic C++ types, and advanced concepts like resource variables and variant types.
Inside TensorFlow - Summaries and TensorBoard
Explore TensorBoard and tf.summary API for data visualization in TensorFlow. Learn about API changes, log directories, event files, and best practices from a TensorFlow engineer.
TF-Agents - A Flexible Reinforcement Learning Library for TensorFlow
Explore TF-Agents, a flexible Deep Reinforcement Learning library for TensorFlow. Learn about recent advancements, implementation techniques, and how to jumpstart your RL projects using this modular open-source tool.
Cloud TPU Pods - AI Supercomputing for Large Machine Learning Problems
Explore Cloud TPU Pods for AI supercomputing, enabling large-scale model parallelism in deep learning. Learn technical details and new TensorFlow features for accelerating machine learning workloads.
Machine Learning for Game Developers - Google I/O 2019
Explore machine learning solutions for game development, from Google-built ML models to custom AI training, addressing challenges traditional programming can't solve.
Getting Started with TensorFlow 2.0 - Google I/O'19
Explore TensorFlow 2.0's user-friendly APIs for creating neural networks. Learn when to use Keras Sequential, Functional, and Subclassing APIs through practical code examples.
Machine Learning on Your Device - The Options
Explore TensorFlow's options for integrating machine learning into mobile apps and edge devices, from model training to deployment using TensorFlow Lite.
Inside TensorFlow - TF Input Pipeline
Explore TensorFlow's tf.data input pipeline, covering data sources, transformations, performance optimizations, and advanced features for efficient data handling in machine learning workflows.
TensorFlow Extended - TFX Overview and Pre-Training Workflow
Explore TensorFlow Extended's pre-training workflow, focusing on data validation and transformation for robust machine learning pipelines. Learn key components and best practices for efficient ML development.
Deep Learning for Fundamental Sciences Using High-Performance Computing
Exploring deep learning applications in fundamental sciences using high-performance computing to uncover universe secrets and improve discovery sensitivity.
Tensor2Tensor - TensorFlow at O’Reilly AI Conference, San Francisco '18
Explore state-of-the-art deep learning models and datasets with Tensor2Tensor, accelerating ML applications like translation, parsing, and image captioning.
Distributed TensorFlow Training - Google I/O 2018
Learn to scale TensorFlow training across multiple GPUs and machines efficiently. Explore distribution strategies, performance optimization techniques, and practical implementation using Google Cloud.
Advances in Machine Learning and TensorFlow - Google I/O '18
Explore cutting-edge machine learning applications in robotics, astronomy, and art through short presentations by top TensorFlow researchers at Google I/O '18.
TensorFlow and Deep Reinforcement Learning, Without a PhD - Google I/O '18
Learn to implement deep reinforcement learning with TensorFlow, teaching a neural network to play Pong using only pixel data. No prior expertise required.
Malarial Cell Classification Using CNN
Learn to classify malarial cells using CNN in Python. Covers dataset acquisition, TensorFlow setup, network design, and result analysis for detecting parasitized and healthy cells.
An Overview of Deep Learning and Neural Networks
Comprehensive introduction to deep learning concepts, covering neural networks, convolution, pooling, normalization, and more. Includes code examples for practical implementation.
Beyond Deep Learning - Learning+Reasoning
Explore advanced AI concepts beyond deep learning, focusing on integrating reasoning with learning for more robust and interpretable systems in this IBM Research lecture.
Denoising RGB Images Using Deep Learning - Noise2Void
Learn to denoise RGB images using Noise2Void deep learning technique, focusing on implementation, data preparation, and model training for improved image quality without clean data.