Alle meine offenen Google Chrome-Tabs

Ich habe damals (Januar 2018) hier alle meine Deep-Learning-Links veröffentlicht, die ich in meinem Browser geöffnet hatte. Haben Sie jemals gewandert, wie viele Tabs ich noch offen habe, entweder weil ich einige von ihnen regelmäßig benutze oder weil ich zu beschäftigt bin mit meinen Forschungsarbeiten und ich sie noch nicht lesen konnte? Antwort: 130 geöffneten Tabs !!!

Ich füge hier alle Links ein, damit ich sie in naher Zukunft besuchen und lesen kann, denn ab jetzt, obwohl ich „The Great Suspender“ benutze, brauche ich meinen Chrome-Browser, um mehr Platz zu haben. Ich habe alle meine offenen Links mit Copy All  Urls Add-on für Google Chrome kopiert. Ich habe diesen Copy All Urls custom Format benutzt: <p>$title - <a href="$url">$url</a><br/></p>.

Ich fordere dich auf, dieses Google Chrome Addon zu installieren und hier als Kommentar alle Links einzufügen, die du geöffnet hast. Vielleicht helfen diese Webseiten auch einigen von euch, interessante Dinge zu finden.

Alle meine offenen Google Chrome-Tabs:

TinyPNG – Compress PNG images while preserving transparency –

Transfer Learning using Keras – Prakash Jay – Medium –

burningion/rich-mans-deep-learning-camera: Building a Self Contained Deep Learning Camera with the NVIDIA Jetson and Python –

A simple way to understand machine learning vs deep learning | Zendesk Blog –

How to Organize Data Labeling for Machine Learning: Approaches and Tools | AltexSoft –

MIT Deep Learning –

Recurrent Neural Networks (LSTM / RNN) Implementation with Keras – Python – YouTube –

Limitations of Deep Learning for Vision, and How We Might Fix Them –

Jeff Chen –

Neural Networks seem to follow a puzzlingly simple strategy to classify images –

A friendly introduction to Recurrent Neural Networks – YouTube –

How to train Keras model x20 times faster with TPU for free –

GAN — Why it is so hard to train Generative Adversarial Networks! –

Convolutional Neural Networks — Simplified – x8 — The AI Community – Medium –

Convolutional Neural Network – Towards Data Science –

More Edge Detection – Foundations of Convolutional Neural Networks | Coursera –

Convolutional Neural Networks (CNN, or ConvNets) – F D – Medium –

QuillBot | Free Paraphrasing Tool – Best Article Rewriter –

3 652 Implement Synonyms – Other Words for Implement –

Checklist for debugging neural networks – Towards Data Science –

Intro To Neural Networks: Biological vs Artificial Neurons – YouTube –

Arduino DC Motor Control Tutorial – L298N | PWM | H-Bridge – HowToMechatronics –

Vision with Core ML – WWDC 2018 – Videos – Apple Developer –

TLM | Convolutional Neural Network (CNN) –

Making floating point math highly efficient for AI hardware – Facebook Engineering –

Convolutional Neural Networks: An Intro Tutorial – Heartbeat –

A Recipe for Training Neural Networks –

16 Awesome OpenCV Functions for your Computer Vision Project! –

Understanding of Convolutional Neural Network (CNN) — Deep Learning –

Why Training a Neural Network Is Hard –

Best Practices for Preparing and Augmenting Image Data for CNNs –

Lessons learned from reproducing ResNet and DenseNet on CIFAR-10 dataset –

Understanding and Coding a ResNet in Keras – Towards Data Science –

Hitchhiker’s Guide to Residual Networks (ResNet) in Keras –

Gender Classifier and Age Estimator using Resnet Convolution Neural Network – YouTube –

How I used Deep Learning to classify medical images with –

Practical Deep Learning for Coders, v3 | course v3 –

Keras Conv2D and Convolutional Layers – PyImageSearch –

Real-time and video processing object detection using Tensorflow, OpenCV and Docker. –


Neural Networks – ResNets – YouTube –

An AI Pioneer Explains the Evolution of Neural Networks | WIRED –

Introduction to Neural Networks – Cezanne Camacho – Machine and deep learning educator. –

Image Classification in 10 Minutes with MNIST Dataset –

Implement Transfer Learning with a generic Code Template – YouTube –

Convolutional Neural Networks Tutorial in TensorFlow – Adventures in Machine Learning –

Keras tutorial – build a convolutional neural network in 11 lines – Adventures in Machine Learning –

Create your first Image Recognition Classifier using CNN, Keras and Tensorflow backend –

Series Introduction – Python Basics 1/10 – YouTube –

How to Develop Competence With Deep Learning for Computer Vision –

Intermediate Topics in Neural Networks – Towards Data Science –

How to rapidly test dozens of deep learning models in Python –

Advanced Topics in Deep Convolutional Neural Networks –

Applied Deep Learning – Part 4: Convolutional Neural Networks –

Best Way to Learn Python (Step-by-Step Guide) – Simpliv LLC –

1. Intro – Quantyca – Medium –

Convolutional Neural Network Tutorial (CNN) | Convolutional Neural Networks With TensorFlow – YouTube –

Where We See Shapes, AI Sees Textures | Quanta Magazine –

Deep Learning For Beginners Using Transfer Learning In Keras –

Deep Learning from the Foundations · –

Teach Yourself Programming in Ten Years –

Exploring Neurons || Transfer Learning in Keras for custom data – VGG-16 – YouTube –

CS231n Convolutional Neural Networks for Visual Recognition – –

CNNs, Part 1: An Introduction to Convolutional Neural Networks – –

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning –

dipanjanS/hands-on-transfer-learning-with-python: Deep learning simplified by transferring prior learning using the Python deep learning ecosystem –

How to Install TensorFlow GPU on Windows – FULL TUTORIAL – YouTube –

How to fine-tune ResNet in Keras and use it in an iOS App via Core ML –

Here’s how you can accelerate your Data Science on GPU –

Transfer Learning for Image Classification using Keras –

Rules-of-thumb for building a Neural Network – Towards Data Science –

Stylizing Video by Example –

Introducing tf-explain, Interpretability for TensorFlow 2.0 –

Stanford DAWN Deep Learning Benchmark (DAWNBench) · –

mlperf/inference: Reference implementations of inference benchmarks –

Towards a metric for the energy efficiency of computer servers – ScienceDirect –

NVIDIA Boosts AI Performance in MLPerf v0.6 | NVIDIA Developer Blog –

Convenient Power Measurements on the Jetson TX2/Tegra X2 Board | Notes on Running DNNs on Embedded Platforms –

Object Detection on NVIDIA Jetson TX2 – Data Driven Investor – Medium –

NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge | NVIDIA Developer Blog –

An Easy Guide to Gauge Equivariant Convolutional Networks –

Metrics_ML_FastPath19 –

Setting benchmarks in machine learning – O’Reilly Media –

Google, Nvidia tout advances in AI training with MLPerf benchmark results | ZDNet –

MLPerf –

Nvidia and Google Post new MLPerf AI Training Results | –

Google and NVIDIA Break MLPerf Records – SyncedReview – Medium –

Cloud TPU Pods break AI training records | Google Cloud Blog –

Reading Between the MLPerf Lines –

6a0120a5580826970c0240a493e942200d-pi (1198×1847) –

MLPerf Design Choices.pdf – file:///C:/Users/SorinLiviu/Desktop/Doctorand%20in%20UPT/anul%203%20doctorat/Idei%20Articole%20anul%203/Energy%20Aware%20Deep%20Learning%20Benchmark%20Metrics/MLPerf%20Design%20Choices.pdf

An Introduction to Graph Theory and Network Analysis (with Python codes) –

Build a Hardware-based Face Recognition System for $150 with the Nvidia Jetson Nano and Python –

ieee Upcoming Conferences for Computer Science & Electronics –

SAMI 2020 –

Neural networks: training with backpropagation. –

Cloud GPUs Tutorial (comparing & using) – YouTube –

Automate the Boring Stuff with Python Programming | Udemy –

Review: YOLOv3 — You Only Look Once (Object Detection) –

Image classification from scratch in keras. Beginner friendly, intermediate exciting and expert refreshing. –

A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning –

Approach pre-trained deep learning models with caution –

Kirk Kaiser – Birding with Python and Machine Learning – PyCon 2018 – YouTube –

Building a Self Contained Deep Learning Camera in Python with NVIDIA Jetson – Make Art with Python – : Orient Power 12.8V 75AH New DIY Solar Battery Option Most Safe Lithium Battery Type LiFePO4 : Garden & Outdoor –

Terasic – All FPGA Main Boards –

Top 15 Evaluation Metrics for Machine Learning with Examples –

Metrics to Evaluate your Machine Learning Algorithm –

MLPerf –

BD-20180604-MLPerf.pdf –

Stanford DAWN Deep Learning Benchmark (DAWNBench) · –

Tutorial on Hardware Accelerators for Deep Neural Networks –

Nvidia, Google Tie in Second MLPerf Training ‚At-Scale‘ Round –

Training Results – MLPerf –

MLPerf-Bench –

How Machine Learning Can Transform The Energy Industry –

1907.10597v3.pdf –

1906.02243.pdf –

tbd-iiswc18.pdf –

Snehil_Metrics_for_Machine_Learning_Workload_Benchmarking.pdf –

1906.11879.pdf –

Boost your CNN image classifier performance with progressive resizing in Keras –

[1801.04381] MobileNetV2: Inverted Residuals and Linear Bottlenecks –

Why MobileNet and Its Variants (e.g. ShuffleNet) Are Fast –

MDZ-Reader | Band | Nature | Nature –

How fast is my model? –

Komplettset 1×100 Watt Monokristallin 5-Busbars 20A Laderegler 12V / 24V Kabel | Solarsets / Komplettangebote | Solarmodule | –×100-watt-monokristallin-5-busbars-20a-laderegler-12v/24v-kabel-1000100m20

Basic motion detection and tracking with Python and OpenCV – PyImageSearch –

Leave a Comment

Diese Website verwendet Akismet, um Spam zu reduzieren. Erfahren Sie mehr darüber, wie Ihre Kommentardaten verarbeitet werden .