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08:29

Recitation 0b | Fundamentals of Numpy – Installing and Initializing arrays (1/8)

Carnegie Mellon University 11785 Introduction to Deep Learning | Spring 22 Notebook link: https://drive.google.com/file/d/110pcUc9fCXnJmLUodtB6_u10cyjNFLg0/view?usp=sharing Reference Material : Numpy User Guide https://numpy.org/doc/stable/user/index.html Numpy Installation – 0:33 Importing Numpy – 1:08 • Randomization Seed – 1:32 Initialization – 2:00 • Empty Array – 2:11 • Zeros Array – 2:26 • Ones Array – 2:41 • Full Array […]
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12:54

Self-Driving Cars – Lecture 11.1 (Object Tracking: Introduction)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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07:08

Recitation 0f | AWS Fundamentals (1/3)

A basic overview of AWS and how to set it up. Detailed tutorial on AWS budgeting and cost management: https://www.youtube.com/watch?v=_MGcpT9a-xg How much vCPU limit should you request (Recommended is 16 or 32): https://www.youtube.com/watch?v=DsuxlkZ4PWM YouTube Source for this AI Video
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36:44

Self-Driving Cars – Lecture 11.3 (Object Tracking: Association)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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09:12

Self-Driving Cars – Lecture 11.4 (Object Tracking: Holistic Scene Understanding)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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138
33:37

PyTorch Turns 5!

PyTorch first released publicly five years ago this week. How did it get here, and what does the future hold for AI frameworks? Meta CTO Mike Schroepfer spoke with machine learning pioneer Yann LeCun, PyTorch co-creator Soumith Chintala and Meta’s PyTorch lead, Lin Qiao. Source of this PyTorch AI Video
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257
00:37

Remove Duplicate Elements From A List In Python (2 Ways)

2 ways how to remove duplicate elements from a list in Python! ⭐ Join Our Discord : https://discord.gg/FHMg9tKFSN ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: https://www.python-engineer.com 🐦 Twitter – https://twitter.com/python_engineer ✉️ Newsletter – https://www.python-engineer.com/newsletter 📸 Instagram – https://www.instagram.com/patloeber 🦾 Discord: https://discord.gg/FHMg9tKFSN ▶️ Subscribe: https://www.youtube.com/channel/UCbXgNpp0jedKWcQiULLbDTA?sub_confirmation=1 ~~~~~~~~~~~~~~ SUPPORT ME ~~~~~~~~~~~~~~ 🅿 Patreon – https://www.patreon.com/patrickloeber #Python #shorts Source of this […]
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35:48

Self-Driving Cars – Lecture 10.5 (Object Detection: 3D Object Detection)

Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen) Course Website with Slides, Lecture Notes, Problems and Solutions: https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/self-driving-cars/ Source of this “Tübingen Machine Learning” AI Video
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06:40

PYTORCH MOBILE AT META SCALE | CHRISTIAN KELLER & LINBIN YU

PyTorch Mobile was launched two years ago at PyTorch Developer Conference 2019. Today, PyTorch Mobile is used at scale. In this talk, Christian Keller and Linbin Yu do a deep dive on a specific use case, person segmentation, and how it is built and deployed at Meta scale. They’ll also cover how on-device AI is […]
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09:43

WHY AND HOW OF SCALING LARGE LANGUAGE MODELS | NICHOLAS JOSEPH

Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Over the past decade, the amount of compute used for the largest training runs has increased at an exponential pace. We’ve also seen in many domains that larger models are able to attain better performance following precise […]
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16:54

PYTORCH, TODAY AND THE FUTURE | LIN QIAO

In this keynote, Lin Qiao (Engineering Director, Meta AI) discusses how the PyTorch team has focused on building features and libraries to accelerate the speed of iteration of this wisdom cycle in the past year. She also goes into detail about continuous obsessions on improving usability and empowering community collaborations. Lin further shares her vision […]
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14:07

Introduction to GANS for Image and Data Generation (7.1)

GANs are a powerful neural network architecture composed of a discriminator and generator. We can teach a GAN to create synthetic data when given an example training set of actual data. In this video, we begin looking at NVIDIA StyleGAN3 and how to use the pretrained network provided by NVIDIA to generate realistic-looking synthetic faces. […]
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15:09

Applications of Deep Neural Networks Course Overview (1.1, Spring 2022)

Spring 2022 Version. Applications of deep neural networks is a course offered in a hybrid format by Washington University in St. Louis. This course introduces Keras deep neural networks and highlights applications that neural networks are particularly adept at handling compared to previous machine learning models. Deep learning is a group of exciting new technologies […]
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16:28

REAL-WORLD RESEARCH TO PRODUCTION AT FIDELITY | AUSTIN HUANG

Learn first-hand about real-world approaches to taking machine learning from research to production at Fidelity. In this talk, Austin Huang (Vice President, AI & Machine Learning, Fidelity) explains how machine learning use cases have changed – evolving from batch prediction pipelines to real-time consumers of unstructured data. These use cases have also given rise to […]
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22:20

Setting Up CUDA, CUDNN, Keras, and TensorFlow on Windows 11 for GPU Deep Learning

Complete walkthrough of installing TensorFlow/Keras with GPU support on Windows 11. We make use of a “pip install” rather than conda, to ensure that we get the latest version of TensorFlow. This requires installing Visual C++, CUDA, CuDNN, as well as the Python libraries. Guide: https://github.com/jeffheaton/t81_558_deep_learning/blob/master/install/manual_setup2.ipynb 0:54 Installation Guides 2:03 Step 1: NVIDIA Video Driver […]
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09:20

TUTEL-MoE-STACK OPTIMIZATION FOR MODERN DISTRIBUTED TRAINING | RAFAEL SALAS & YIFAN XIONG

The Mixture-of-Experts (MoE) is a sparsely activated deep learning model architecture that has sublinear compute costs with respect to their parameters. MoE is one of the few scalable approaches for training trillion-parameter scale deep learning models. This talk will present Tutel, an open-source project built with the Pytorch framework. Tutel is being actively developed by […]
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10:41

MODEL SERVING IN PYTORCH | GEETA CHAUHAN

Deploying ML models in Production and scaling your ML services still continue to be big challenge. TorchServe, the model serving solution for PyTorch solves this problem and has now evolved into a multi-platform solution that can run on-prem or on any cloud with integrations for major OSS platforms like Kubernetes, MLflow, Kubeflow Pipelines, KServe. This […]
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