Deep Learning Fundamentals - Full Stack Deep Learning
Comprehensive overview of deep learning basics, covering neural networks, learning problems, optimization techniques, and architectural considerations for AI practitioners.
Coding a Neural Network - Full Stack Deep Learning - Spring 2021
Code a neural network from scratch using Google Colab, NumPy, PyTorch, and Keras. Learn linear regression and multi-layer perception models through hands-on implementation and visualization.
Andrej Karpathy on AI at Tesla - Full Stack Deep Learning - August 2018
Explore Tesla's AI innovations with Andrej Karpathy, delving into cutting-edge deep learning applications in autonomous driving and computer vision.
Yangqing Jia on Deep Learning Frameworks - August 2018
Explore deep learning frameworks with Alibaba's VP AI, covering Caffe, Caffe2, and TensorFlow development insights from a pioneering expert in the field.
Richard Socher on NLP at Salesforce - Full Stack Deep Learning - March 2019
Explore cutting-edge NLP applications at Salesforce with Richard Socher, discussing deep learning advancements and their impact on natural language processing in enterprise settings.
Troubleshooting Deep Neural Networks - Full Stack Deep Learning - March 2019
Learn effective strategies for diagnosing and fixing issues in deep neural networks, including a decision tree for systematic troubleshooting and performance optimization.
Lukas Biewald on Founding Weights & Biases and FigureEight - Full Stack Deep Learning - March 2019
Insights from AI industry pioneer on founding data labeling and ML tools companies, exploring challenges, breakthroughs, and the future of machine learning applications.
Introduction to the Text Recognizer Project - Full Stack Deep Learning - March 2019
Hands-on labs introducing text recognition project, covering setup, data handling, model creation, training, and deployment in a full-stack deep learning context.
Setting Up Machine Learning Projects - Full Stack Deep Learning - March 2019
Learn best practices for planning and setting up machine learning projects, including prioritizing projects, choosing metrics, and creating baselines. Gain insights into the lifecycle of ML projects and practical examples.
Computer Vision Dog Breed Classification with Convolutional Neural Networks, TensorFlow and Kubeflow
Develop a dog breed classification system using TensorFlow, Kubeflow, and convolutional neural networks. Compare AI predictions with DNA results for accuracy.
Empower Autonomous Driving with Cloud Native Serverless Technologies
Explore how cloud-native serverless technologies empower autonomous driving, addressing challenges in data archiving, resource management, and architecture flexibility for complex use cases and numerous modules.
Cloud Native Machine Learning - Automated Anomaly Detection
Explore automated anomaly detection techniques for machine learning, enhancing your ability to identify and respond to data irregularities in cloud-native environments.
The State of Production MLOps in the Cloud Native Ecosystem
Explore production MLOps challenges, best practices, and tools in the Cloud Native ecosystem. Learn key principles for deploying, monitoring, and scaling ML systems effectively.
Accelerating High-Performance Machine Learning at Scale in Kubernetes
Hands-on guide for deploying optimized machine learning models in cloud native ecosystems, focusing on GPT-2 NLP model deployment in Kubernetes using ONNX Runtime and Seldon Core Triton server.
Five Ways to Understand Kubernetes Networking for Performance-Intensive Workloads
Explore Kubernetes networking innovations for high-performance workloads. Compare CNI implementations using real-world benchmarks to analyze performance in scientific computing and machine learning.
Training AI to Code Using Project CodeNet - Largest Code Dataset
Explore Project CodeNet's vast code dataset and learn how to leverage it for machine learning tasks using MLX, including code classification and complexity analysis on Kubernetes.
Innovations in Theoretical Computer Science 2020 - Session 9
Cutting-edge research in theoretical computer science, covering topics like distribution testing, random object sampling, probability distribution learning, agreement expansion, neural networks, and influence maximization.
Machine Learning - Lecture 10
Explore advanced machine learning concepts, focusing on deep learning techniques and neural network architectures for complex data analysis and pattern recognition.
Smart Matching for Screening - Using AI to Tackle Crisis KYC
Leverage AI and NLP to enhance screening systems, reduce false positives, and improve accuracy in KYC processes during crisis periods. Live demo and insights from experts included.
Understanding Names with Neural Networks - Session 2
Explore neural network-based approaches for cross-language name matching, focusing on English/Japanese pairs and comparing traditional methods with advanced deep learning techniques.
Understanding Names with Neural Networks - Session 1
Explore neural network approaches for cross-language name matching, focusing on English/Japanese. Learn about seq2seq models, LSTMs, and CNNs for improved accuracy in transliteration and scoring.