Neural Networks by Example
Explore neural networks' principles, applications, and real-life examples using MNIST dataset. Learn about convolutional networks and their use in image recognition and self-driving cars.
TensorFlow in Three Sentences
Explore TensorFlow's capabilities, limitations, and applications in AI. Learn key concepts, required knowledge, and practical methods for tackling computer vision and natural language processing tasks.
Networks Are Like Onions - Practical Deep Learning with TensorFlow
Hands-on tutorial on building neural networks with TensorFlow for computer vision and NLP tasks. Learn key deep learning concepts and gain practical experience in shaping network architectures.
Deep Learning with TensorFlow
Interactive TensorFlow session: build deep learning models from basics to advanced concepts. Hands-on approach to creating and using models in applications, suitable for beginners and experienced developers alike.
Deep Learning with TensorFlow
Explore TensorFlow's fundamentals and create deep learning models through interactive discussions. Gain practical skills to develop and implement AI in your applications.
How to Use ML.NET to Write Crushing Metal Riffs
Explore deep learning with ML.NET to generate metal riffs. Learn about neural networks, RNNs, and guitar tablatures while discovering how to apply machine learning to music composition.
Deep Learning in Microsoft Azure - CNTK, CaffeOnSpark and Tensorflow
Explore Microsoft's approach to Deep Learning using Cognitive Toolkit, Tensorflow, and CaffeOnSpark in Azure, understanding its business and technical benefits.
TensorFlow Tutorial - Custom Dataset for Images
Learn efficient methods to load custom image datasets in TensorFlow using structured subfolders, CSV annotations, or single folders. Covers image_dataset_from_directory, ImageDataGenerator, and more.
TensorFlow Tutorial - Neural Networks with Sequential and Functional API
Learn to build neural networks in TensorFlow 2.0 using Keras Sequential and Functional API. Covers model creation, layer feature extraction, and model summaries with hands-on MNIST dataset examples.
TensorFlow Tutorial - Tensor Basics
Comprehensive guide to essential tensor operations in TensorFlow 2.0, covering initialization, casting, math operations, indexing, and reshaping. Builds foundation for neural network development.
Inside TensorFlow - TF Filesystems
Explore TensorFlow Filesystems with a deep dive into its architecture, Python integration, saved model loading, and plugin system for efficient data handling and model management.
Machine Learning Software Ecosystems: Frameworks, Compilers, and Models - Part 1
Explore the landscape of ML frameworks, compilers, and models while understanding performance considerations for various hardware platforms and edge computing requirements.
Interpretability Tools as Feedback Loops in Machine Learning Training
Explore machine learning interpretability techniques to optimize training sequences and develop more effective models without increasing compute or training time.
Real-Time Threat Detection Using Machine Learning and Apache Kafka
Explore real-time threat detection using machine learning, Apache Kafka, and ksqlDB. Learn about ZIEM, a network mapping and intrusion detection platform that generates instant network diagrams for quick anomaly detection.
Code Walkthrough for PINNs in Burgers Equation
Explore Physics-Informed Neural Networks (PINNs) applied to the Burgers equation through a detailed code walkthrough.
End-to-end Modern Machine Learning in Production - Part 2
Explore RLHF, efficient training techniques, on-device ML, and optimizations in modern machine learning production with insights from a Hugging Face lead engineer.
A Python Developer's Guide to AI in 2024
Comprehensive guide for Python developers on AI trends in 2024, covering machine learning, neural networks, computer vision, LLMs, RAG, and AI agents.
3D U-Net for Semantic Segmentation
Learn to implement 3D U-Net for semantic segmentation on volumetric data like FIB-SEM, CT, and MRI. Covers data preparation, model architecture, training, and multichannel image processing using Python libraries.
Multi-Class Language Classification With BERT in TensorFlow
Learn to build a multi-class language classification model using BERT and TensorFlow. Covers data preprocessing, model architecture, training, and prediction with transformers for NLP tasks.
Deep Dive: Array API Tests in Ivy - Tutorial 14
Explore the Array API test suite integration in Ivy's testing pipeline, covering standard implementation, test execution, and skipping methods.
Fixing the Image Problems of the Web Using Machine Learning
Explore how machine learning can address web image issues, enhancing user experience and accessibility. Learn innovative techniques for optimizing visual content on the internet.