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How to learn tensorflow as a noob?
I am a mobile app developer. I have been working in IT for past 5 years. I took a Udemy course on TensforFlow from Zero to mastery. I thought as I have decent knowledge of software development I might pick up TensorFlow pretty quickly without really knowing the basics of machine learning but I was so wrong as I am having tough time understanding Tensorflow from the course. Everyone keep saying learn linear algebra, pandas, keras , scikit learn etc and a bunch of stuff. This is too much for me. For now I just want to learn how to create an ML model with a given data(Data can be anything image, text etc) and use that model in my web and mobile apps. I know there is something call TensorFlow lite which I can use in my apps directly but what is the bare minimum requirement I need to know before I start learning TensorFlow so I can easily pick it up later.
Also the the Udemy course which I took seems to be pretty good and lot of people seems to be liking it so I don’t think it really is the instructor’s fault as I don’t have my basics clear.
If anyone has any udemy courses that they can point me to would be great. I am looking more on practical approach and not jus theory boring stuff
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Happy Thanksgiving, members. It’s a very special GFN Thursday. As the official kickoff to what’s sure to be a busy holiday season for our members around the globe, this week’s GFN Thursday brings a few reminders of the joys of PC gaming in the cloud. Plus, kick back for the holiday with four new games Read article >
The post A Very Thankful GFN Thursday: New Games, GeForce NOW Gift Cards and More appeared first on The Official NVIDIA Blog.
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How fast is tensorflow?
Hello everybody!
First of all, I know nothing about tensorflow YET. But I’m curious about the image classification / recognition feature and I want to learn it. But I have some questions regarding it.
Lets say, I have 1000 pics about differenct car parts / products (also 1000 parts)and they are fix,static, they never change. Tensorflow should recognize 1 product from those 1000. How fast will tensorflow give an output? How does the speed change if I would have like 10.000, or, just 100 products? Is this actually good use case for tensorflow? + How accurate will be the output?
Thanks for the answer in advance!
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I have used Tensorflow object detection API ( https://github.com/tensorflow/models/tree/master/research/object_detection ) when I needed to do transfer learning of object detection models in the past 2 years. In most cases, I have used the trained models both in Tensorflow during development ( full version not TFLite ) on desktop as well as in TFLite after converting them to run on edge.
Some of the edge applications require a high FPS and therefore need to accelerate the inference using a Coral edge TPU. A constant issue with this approach has been that most model architectures in the Tensorflow object detection zoo are not possible to quantize and use with the Coral TPU. Some SSD models even fail or throw an Exception when trying to convert them to TFLite without quantization, although the documentation states that SSD models are supported.
I saw that the Tensorflow Lite Model maker ( https://www.tensorflow.org/lite/tutorials/model_maker_object_detection ) nowadays has support for transfer learning of EfficientDet models, including quantization and compilation for Coral. TFLite model maker also supports saving to “saved model” format. If I am not mistaken, It should then be possible to save the trained model both as .tflite for use in TFLite with Coral on edge and as saved_model for use with Tensorflow on desktop during development.
Does anyone have experience to share from working with Tensorflow lite model maker for object detection and then deployment on edge with Coral TPU? It would be valuable to hear what works well and what surprises / bugs to expect.
Thanks!
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NVIDIA created content for AWS re:Invent, helping developers learn more about applying the power of GPUs to reach their goals faster and more easily.
See the latest innovations spanning from the cloud to the edge at AWS re:Invent. Plus, learn more about the NVIDIA NGC catalog—a comprehensive collection of GPU-optimized software.
Working closely together, NVIDIA and AWS developed a session and workshop focused on learning more about NVIDIA GPUs and providing hands-on training on NVIDIA Jetson modules.
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More information
How to Select the Right Amazon EC2 GPU Instance and Optimize Performance for Deep Learning
Session ID: CMP328-S
Get all the information you need to make an informed choice for which Amazon EC2 NVIDIA GPU instance to use and how to get the most out of it by using GPU-optimized software for your training and inference workloads.
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Building a people counter with anomaly detection using AWS IoT and ML
Session ID: IOT306
Get started with AWS IoT Greengrass v2, NVIDIA DeepStream, and Amazon SageMaker Edge Manager with computer vision in this workshop. Learn how to make and deploy a video analytics pipeline and build a people counter and deploy it to an NVIDIA Jetson Nano edge device.
This workshop is being delivered by Ryan Vanderwerf, Partner Solutions Architect, and Yuxin Yang, AI/ML IoT Architect.
Virtual content
APN.TV Segment: Simplified AI Model Deployment with Triton Inference Server
Join this session to learn how to use NVIDIA Triton in your AI workflows and maximize the AI performance on your GPUs and CPUs.
NVIDIA Triton is an open source inference-serving software to deploy deep learning and ML models from any framework (TensorFlow, TensorRT, PyTorch, OpenVINO, ONNX Runtime, XGBoost, or custom) on GPU‑ or CPU‑based infrastructure.
Shankar Chandrasekaran, Sr. Product Marketing Manager of NVIDIA, discusses model deployment challenges, how NVIDIA Triton simplifies deployment and maximizes performance of AI models, how to use NVIDIA Triton on AWS, and a customer use case.
APN.TV Segment: Build ML Solutions Faster with NVIDIA NGC Catalog on AWS Marketplace
In this session, Abhilash Somasamudramath, NVIDIA Product Manager of AI Software, will show how to use free GPU-optimized software available on the NGC catalog in AWS Marketplace to achieve your ML goals.
ML has transformed many industries as companies adopt AI to improve operational efficiencies, increase customer satisfaction, and gain a competitive edge. However, the process of training, optimizing, and running ML models to build AI-powered applications is complex and requires expertise.
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theCUBE interview with Ian Buck, GM and Vice President of Accelerated Computing at NVIDIA
Hear Ian Buck discuss the latest trends in ML and AI, how NVIDIA is partnering with AWS to deliver accelerated computing solutions, and how NVIDIA makes accessing AI solutions easier than ever.
High performance CUTLASS template abstractions support matrix multiply operations (GEMM), Convolution AI, and improved Strided-DGrad.
NVIDIA continues to enhance CUTLASS to provide extensive support for mixed-precision computations, providing specialized data-movement, and multiply-accumulate abstractions. Today, NVIDIA is announcing the availability of CUTLASS version 2.8.
Download the free CUTLASS v2.8 software.
What’s new
- Emulated single-precision GEMM and Convolution (up to 48TFLOPs)
- Grouped GEMM concept
- Improved Strided-DGrad
See the CUTLASS Release Notes for more information.
About CUTLASS
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels, and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
CUTLASS decomposes these “moving parts” into reusable and modular software components abstracted by C++ template classes. These thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.
To support a wide variety of applications, CUTLASS provides extensive support for mixed-precision computations, providing specialized data-movement, and multiply-accumulate abstractions for:
- Half-precision floating point (
FP16), BFloat16 (BF16), and Tensor Float 32 (TF32) data types. - Single-precision floating point (
FP32) data type. - Double-precision floating point (
FP64) data type. - Integer data types (
4band8b). - Binary data types (
1b).
Furthermore, CUTLASS demonstrates warp-synchronous matrix multiply operations targeting the programmable, high-throughput Tensor Cores implemented on NVIDIA Volta, Turing, and Ampere architectures.
CUTLASS implements high-performance convolution (implicit GEMM). Implicit GEMM is the formulation of a convolution operation as a GEMM. This allows CUTLASS to build convolutions by reusing highly optimized warp-wide GEMM components and below.
Learn more
- A Deep Dive into the Latest HPC Software (GTC: A31050)
- Accelerating Convolution with Tensor Cores in CUTLASS (GTC: S31883)
- Recent Developments in NVIDIA Math Libraries (GTC: S31754)
- A Deep Dive into the Latest HPC Software (GTC: S31286)
- Tensor Core-Accelerated Math Libraries for Dense and Sparse Linear Algebra in AI and HPC (GTC: CWES1098)
- CUTLASS Product Documentation
Recent Developer Blog posts
Have you been looking for pretrained vision transformer models in TensorFlow? Have you been frustrated that pretrained models are available only in PyTorch? And JAX…
Let me introduce TensorFlow Image Models (tfimm), a TF port of the PyTorch timm library, which in version v0.1.1 provides 37 pretrained vision transformers of the ViT and DeiT varieties.
The list of available models will grow in upcoming releases.
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