Lecture 1 - Introduction to AI Systems Hardware - Part 1
Lecture 2 - Introduction to AI Systems Hardware - Part 2
Lecture 3 - Introduction to AI Accelerators, GPUs
Lecture 4 - Introduction to Operating Systems, Virtualization, Cloud - Part 1
Lecture 5 - Introduction to Operating Systems, Virtualization, Cloud - Part 2
Lecture 6 - Introduction to Containers and IDE Dockers - Part 1
Lecture 7 - Introduction to Containers and IDE Dockers - Part 2
Lecture 8 - Scheduling and Resource Management - Part 1
Lecture 9 - Scheduling and Resource Management - Part 2
Lecture 10 - DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services - Part 1
Lecture 11 - DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services - Part 2
Lecture 12 - DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Session II - Part 1
Lecture 13 - DeepOps: Deep Dive into Kubernetes with deployment of various AI based Services Session II - Part 2
Lecture 14 - Design principles for Building High Performance Clusters - Part 1
Lecture 15 - Design principles for Building High Performance Clusters - Part 2
Lecture 16 - Design principles for Building High Performance Clusters - Part 3
Lecture 17 - Design principles for Building High Performance Clusters - Part 4
Lecture 18 - Introduction to Pytorch - Part 1
Lecture 19 - Introduction to Pytorch - Part 2
Lecture 20 - Introduction to Pytorch - Part 3
Lecture 21 - Introduction to Pytorch - Part 4
Lecture 22 - Profiling with DLProf Pytorch Catalyst - Part 1
Lecture 23 - Profiling with DLProf Pytorch Catalyst - Part 2
Lecture 24 - Introduction to TensorFlow - Part 1
Lecture 25 - Introduction to TensorFlow - Part 2
Lecture 26 - Accelerated TensorFlow - Part 1
Lecture 27 - Accelerated TensorFlow - Part 2
Lecture 28 - Accelerated TensorFlow - XLA Approach - Part 1
Lecture 29 - Accelerated TensorFlow - XLA Approach - Part 2
Lecture 30 - Optimizing Deep learning Training: Automatic Mixed Precision - Part 1
Lecture 31 - Optimizing Deep learning Training: Automatic Mixed Precision - Part 2
Lecture 32 - Optimizing Deep learning Training: Transfer Learning - Part 1
Lecture 33 - Optimizing Deep learning Training: Transfer Learning - Part 2
Lecture 34 - Fundamentals of Distributed AI Computing Session 1 - Part 1
Lecture 35 - Fundamentals of Distributed AI Computing Session 1 - Part 2
Lecture 36 - Fundamentals of Distributed AI Computing Session 2 - Part 1
Lecture 37 - Fundamentals of Distributed AI Computing Session 2 - Part 2
Lecture 38 - Distributed Deep Learning using Tensorflow and Horovod
Lecture 39 - Challenges with Distributed Deep Learning Training Convergence
Lecture 40 - Fundamentals of Accelerating Deployment - Part 1
Lecture 41 - Fundamentals of Accelerating Deployment - Part 2
Lecture 42 - Accelerating neural network inference in PyTorch and TensorFlow - Part 1
Lecture 43 - Accelerating neural network inference in PyTorch and TensorFlow - Part 2
Lecture 44 - Accelerated Data Analytics - Part 1
Lecture 45 - Accelerated Data Analytics - Part 2
Lecture 46 - Accelerated Data Analytics - Part 3
Lecture 47 - Accelerated Data Analytics - Part 4
Lecture 48 - Accelerated Machine Learning
Lecture 49 - Scale Out with DASK
Lecture 50 - Web visualizations to GPU accelerated crossfiltering - Part 1
Lecture 51 - Web visualizations to GPU accelerated crossfiltering - Part 2
Lecture 52 - Accelerated ETL Pipeline with SPARK - Part 1
Lecture 53 - Accelerated ETL Pipeline with SPARK - Part 2
Lecture 54 - Applied AI: Smart City (Intelligent Video Analytics) Session 1 - Part 1
Lecture 55 - Applied AI: Smart City (Intelligent Video Analytics) Session 1 - Part 2
Lecture 56 - Applied AI: Smart City (Intelligent Video Analytics) Session 2 Deepstream - Part 1
Lecture 57 - Applied AI: Smart City (Intelligent Video Analytics) Session 2 Deepstream - Part 2
Lecture 58 - Applied AI: Health care Session I - Part 1
Lecture 59 - Applied AI: Health care Session I - Part 2
Lecture 60 - Applied AI: Health care Session II - Part 1
Lecture 61 - Applied AI: Health care Session II - Part 2