MLOps - Machine Learning Operations
Learn MLOps essentials: deployment, monitoring, feature stores, and CI/CD pipelines. Hands-on with Azure, Kubernetes, TFX, and cloud platforms for efficient machine learning operations.
Deep Learning
Explore deep learning fundamentals, computer vision, TensorFlow, Keras, transfer learning, and advanced topics like TPUs in this comprehensive introduction to neural networks.
Neural Network Programming - Deep Learning with PyTorch
Learn to build and train neural networks using PyTorch, covering tensor operations, CNN architecture, data preparation, training loops, and performance optimization techniques for deep learning projects.
Learning Machine Learning Efficiently
Discover an efficient approach to learning machine learning, exploring the landscape, optimal paths, and practical strategies for mastering this complex field.
Tensorflow Object Detection with Python - Full Course with 3 Projects
Comprehensive guide to Tensorflow Object Detection: from setup to advanced projects. Learn to build, train, and deploy models for gesture, defect, and sentiment detection using Python, JavaScript, and Raspberry Pi.
Build a Deep Face Detection Model With Python and Tensorflow - Full Course
Comprehensive guide to building a face detection model using TensorFlow and Python, covering data collection, augmentation, model creation, training, and real-time testing for practical implementation.
Creating a Machine Learning GUI App with Taipy and TensorFlow
Build a stylish GUI for image classification using Taipy and TensorFlow. Learn to process images, create neural networks, and design user-friendly interfaces for machine learning applications.
Natural Language Processing - Zero to Hero
Explore NLP fundamentals with TensorFlow, from tokenization to sentiment analysis and AI-generated poetry, in this beginner-friendly series led by Laurence Moroney.
Programming LSTM with Keras and TensorFlow
Explore LSTM and GRU layers for recurrent neural networks in Keras, laying the groundwork for NLP and time series prediction with practical examples and implementations.
Practical Machine Learning with Tensorflow
We will cover the basics of Tensorflow and Machine Learning in the initial sessions and advanced topics in the latter part. After this course, the students will be able to build ML models using Tensorflow.
Current Status of TinyML and the Enormous Opportunities Ahead - Panel Discussion
Panel discussion exploring tinyML's current state and future potential, featuring experts from Qualcomm and Google. Covers applications, challenges, privacy concerns, and emerging opportunities in ultra-low power embedded AI computing.
Deep Learning Basics with Python, TensorFlow and Keras
Master deep learning fundamentals using Python, TensorFlow, and Keras. Explore CNNs, RNNs, data loading, model analysis, optimization, and practical applications in cryptocurrency prediction.
Practical Machine Learning with Tensorflow
Learn essential machine learning concepts and apply them using TensorFlow, covering key topics from basic principles to deep learning and data visualization.
Tensor Processing Units - TPUs
Explore Tensor Processing Units (TPUs) for accelerated deep learning, from basics to advanced optimization techniques and real-world applications.
Applications of Deep Neural Networks for TensorFlow and Keras
Comprehensive exploration of deep neural networks using TensorFlow and Keras, covering image processing, GANs, transfer learning, NLP, reinforcement learning, and practical applications.
From Native Code Gems to Java Treasures with Jextract
Explore the power of Project Panama and the Foreign Function and Memory API (FFM API) in this 33-minute Devoxx conference talk. Discover how to seamlessly integrate system-level APIs and libraries written in languages like C, OpenGL, TensorFlow, Rust, an…
Remove Objects from Images Like Magic - Step-by-Step Guide
Learn to remove objects from images using Inpainter, combining semantic segmentation and EdgeConnect. Master installation, single and multiple object removal for photo editing and data augmentation.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
From Concept to Production: Building a Production-Ready ML Pipeline - Part 2
Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.
Dynatrace for AI Observability: Monitoring OpenAI, TensorFlow, and Other AI APIs
Gain insights into AI model behavior, performance, and costs using Dynatrace. Monitor OpenAI and TensorFlow APIs, optimize performance, and understand AI observability through practical demonstrations.
Low Latency Neural Network Inference for ML Ranking Applications - Yelp Case Study
Explore Yelp's ML platform overhaul for low-latency neural network inference in ranking applications. Learn about Tensorflow model training, deployment using MLEAP, and integration with Nrtsearch for improved performance.