Using Kubernetes for Machine Learning Frameworks
Explore Kubernetes for machine learning frameworks, covering setup, training, and inference phases. Learn to deploy TensorFlow, Apache MXNet, and PyTorch on Kubernetes clusters for efficient ML model production.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions with Apache Spark. Explore distributed computing, MLflow, feature engineering, and integrating Spark with deep learning frameworks like TensorFlow and PyTorch.
The Importance of Forgetting for Both Humans and Machines
Explore the crucial role of forgetting in human and machine learning, its impact on adapting to change, and state-of-the-art processes for updating ML models in production environments.
Secure and Scalable Anomaly-Based Network Intrusion Detection
Presentation on a secure, scalable framework for anomaly-based network intrusion detection, implemented in Go. Explores feature extraction, data collection, and machine learning applications for detecting network threats.
Building Voice-First Flutter Apps
Explore voice-first Flutter app development using various integrations, including Flutter packages, ML solutions, third-party tools, and platform-specific features for Android and iOS.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.
Real-World Machine Learning with TensorFlow and Cloud ML
Explore real-world applications of TensorFlow and Cloud ML in various industries, including agriculture, automotive, and food manufacturing, showcasing innovative solutions to business challenges.
Making Machine Learning Accessible Using Keras
Explore Keras, a user-friendly deep learning API, to make AI accessible for solving business problems. Learn about its features, APIs, model building, and integration with TensorFlow.
End-to-End Deep Learning Pipeline for Self-Driving Cars
Explore the end-to-end deep learning pipeline for self-driving cars, covering data collection, processing, model training, evaluation, and deployment, with insights on common mistakes and future developments.
Deep Learning for Mobile Devices
Strategies for implementing efficient convolutional neural networks on mobile devices, covering model optimization, hardware considerations, and on-device training techniques.
How to Build Connected Products
Learn to create connected products using IoT building blocks, APIs, and local platforms. Explore monetization, use cases, hardware compatibility, and security considerations for IoT development.
Tensorflow on Open Source GPUs
Explore efforts to enable TensorFlow on open-source GPUs using SYCL, LLVM, and OpenCL. Learn about challenges and progress in deploying AI/ML frameworks without proprietary NVIDIA CUDA stack.
Getting Started with Automatic Differentiation
Explore automatic differentiation in Python, its applications in optimization and computational art, using libraries like JAX, TensorFlow, and PyTorch. Gain intuition for derivatives and gradients.
Deep Learning with Python & TensorFlow
Explore deep learning with TensorFlow: compare Python ML libraries, create data flow graphs, and learn deployment strategies for production systems.
Introduction to TensorFlow
Aprende los conceptos básicos de deep learning y TensorFlow, desde la instalación hasta la creación de redes neuronales, a través de ejemplos prácticos y visualizaciones amigables.
From Linear Algebra to Machine Learning
Explore the mathematical foundations of machine learning, from linear algebra to optimization, with practical Python implementations using NumPy, SciPy, and TensorFlow.
Building a Powerful Pet Detector in Notebooks
Learn to build and deploy an AI-powered pet breed detector using Azure Notebooks, TensorFlow, and transfer learning techniques. Optimize the model for improved accuracy and create a web service.
Audio Classification with Machine Learning
Learn to build practical machine learning models for audio classification using spectrograms, CNNs, and techniques like transfer learning and data augmentation with Keras and TensorFlow.
Astro Pi - Python on the International Space Station
Explore Python programming on the International Space Station with Astro Pi, a collaboration between Raspberry Pi Foundation and ESA, offering exciting opportunities for students to conduct space experiments.
Machine Learning for Everyone
Discover the essentials of machine learning, neural networks, and reinforcement learning in this engaging talk. Gain practical insights and witness a mind-blowing demo to kickstart your ML journey.
Building Reproducible Distributed Applications at Scale
Explore challenges and solutions for packaging Python code in distributed computing environments, focusing on shipping methods, pickling, self-contained executables, and real-world examples with PySpark and S3 storage.