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>.
Alle meine offenen Google Chrome-Tabs:
TinyPNG – Compress PNG images while preserving transparency – https://tinypng.com/
Transfer Learning using Keras – Prakash Jay – Medium – https://medium.com/@14prakash/transfer-learning-using-keras-d804b2e04ef8
burningion/rich-mans-deep-learning-camera: Building a Self Contained Deep Learning Camera with the NVIDIA Jetson and Python – https://github.com/burningion/rich-mans-deep-learning-camera
A simple way to understand machine learning vs deep learning | Zendesk Blog – https://www.zendesk.com/blog/machine-learning-and-deep-learning/
How to Organize Data Labeling for Machine Learning: Approaches and Tools | AltexSoft – https://www.altexsoft.com/blog/datascience/how-to-organize-data-labeling-for-machine-learning-approaches-and-tools/
MIT Deep Learning – https://deeplearning.mit.edu/
Recurrent Neural Networks (LSTM / RNN) Implementation with Keras – Python – YouTube – https://www.youtube.com/watch?v=iMIWee_PXl8
Limitations of Deep Learning for Vision, and How We Might Fix Them – https://thegradient.pub/the-limitations-of-visual-deep-learning-and-how-we-might-fix-them/
Jeff Chen – http://thisisjeffchen.com/
Neural Networks seem to follow a puzzlingly simple strategy to classify images – https://medium.com/bethgelab/neural-networks-seem-to-follow-a-puzzlingly-simple-strategy-to-classify-images-f4229317261f
A friendly introduction to Recurrent Neural Networks – YouTube – https://www.youtube.com/watch?v=UNmqTiOnRfg
How to train Keras model x20 times faster with TPU for free – https://medium.com/swlh/how-to-train-keras-model-x20-times-faster-with-tpu-for-free-cac6cf5089cb
GAN — Why it is so hard to train Generative Adversarial Networks! – https://medium.com/@jonathan_hui/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b
Convolutional Neural Networks — Simplified – x8 — The AI Community – Medium – https://medium.com/x8-the-ai-community/cnn-9c5e63703c3f
Convolutional Neural Network – Towards Data Science – https://towardsdatascience.com/covolutional-neural-network-cb0883dd6529
More Edge Detection – Foundations of Convolutional Neural Networks | Coursera – https://www.coursera.org/lecture/convolutional-neural-networks/more-edge-detection-8Donz
Convolutional Neural Networks (CNN, or ConvNets) – F D – Medium – https://medium.com/@phidaouss/convolutional-neural-networks-cnn-or-convnets-d7c688b0a207
QuillBot | Free Paraphrasing Tool – Best Article Rewriter – https://quillbot.com/app
3 652 Implement Synonyms – Other Words for Implement – https://www.powerthesaurus.org/implement/synonyms
Checklist for debugging neural networks – Towards Data Science – https://towardsdatascience.com/checklist-for-debugging-neural-networks-d8b2a9434f21
Intro To Neural Networks: Biological vs Artificial Neurons – YouTube – https://www.youtube.com/watch?v=SuOlDvs-_Ek&t=1228s
Arduino DC Motor Control Tutorial – L298N | PWM | H-Bridge – HowToMechatronics – https://howtomechatronics.com/tutorials/arduino/arduino-dc-motor-control-tutorial-l298n-pwm-h-bridge/
Vision with Core ML – WWDC 2018 – Videos – Apple Developer – https://developer.apple.com/videos/play/wwdc2018/717
TLM | Convolutional Neural Network (CNN) – https://www.thelearningmachine.ai/cnn
Making floating point math highly efficient for AI hardware – Facebook Engineering – https://engineering.fb.com/ai-research/floating-point-math/
Convolutional Neural Networks: An Intro Tutorial – Heartbeat – https://heartbeat.fritz.ai/a-beginners-guide-to-convolutional-neural-networks-cnn-cf26c5ee17ed
A Recipe for Training Neural Networks – http://karpathy.github.io/2019/04/25/recipe/
16 Awesome OpenCV Functions for your Computer Vision Project! – https://www.analyticsvidhya.com/blog/2019/03/opencv-functions-computer-vision-python/?utm_source=facebook.com&utm_medium=social&fbclid=IwAR0gk_gwLAq6Mmj1p0tK97K_dIntpG4kUI6hf9z1lQv_IL4edmK61IupC2M
Understanding of Convolutional Neural Network (CNN) — Deep Learning – https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
Why Training a Neural Network Is Hard – https://machinelearningmastery.com/why-training-a-neural-network-is-hard/
Best Practices for Preparing and Augmenting Image Data for CNNs – https://machinelearningmastery.com/best-practices-for-preparing-and-augmenting-image-data-for-convolutional-neural-networks/
Lessons learned from reproducing ResNet and DenseNet on CIFAR-10 dataset – https://medium.com/@wwwbbb8510/lessons-learned-from-reproducing-resnet-and-densenet-on-cifar-10-dataset-6e25b03328da
Understanding and Coding a ResNet in Keras – Towards Data Science – https://towardsdatascience.com/understanding-and-coding-a-resnet-in-keras-446d7ff84d33
Hitchhiker’s Guide to Residual Networks (ResNet) in Keras – https://towardsdatascience.com/hitchhikers-guide-to-residual-networks-resnet-in-keras-385ec01ec8ff
Gender Classifier and Age Estimator using Resnet Convolution Neural Network – YouTube – https://www.youtube.com/watch?v=atJmJ8tNc3U&list=LLpoz6Qetfqwdg_bWRdXJqNQ&index=2&t=0s
How I used Deep Learning to classify medical images with Fast.ai – https://www.freecodecamp.org/news/how-i-used-deep-learning-to-classify-medical-images-with-fast-ai-cc4cfd64173c/
Practical Deep Learning for Coders, v3 | fast.ai course v3 – https://course.fast.ai/
Keras Conv2D and Convolutional Layers – PyImageSearch – https://www.pyimagesearch.com/2018/12/31/keras-conv2d-and-convolutional-layers/
Real-time and video processing object detection using Tensorflow, OpenCV and Docker. – https://towardsdatascience.com/real-time-and-video-processing-object-detection-using-tensorflow-opencv-and-docker-2be1694726e5
UNDERSTANDING RESIDUAL NETWORKS – Towards Data Science – https://towardsdatascience.com/understanding-residual-networks-9add4b664b03
Neural Networks – ResNets – YouTube – https://www.youtube.com/watch?v=ahkBkIGdnWQ
An AI Pioneer Explains the Evolution of Neural Networks | WIRED – https://www.wired.com/story/ai-pioneer-explains-evolution-neural-networks/?fbclid=IwAR2MS0ES356i2lLbRzXLXkvxeg3ZMvkK9ODJYss3gBLTfDpH1Y1Za7T2ZMg
Introduction to Neural Networks – Cezanne Camacho – Machine and deep learning educator. – https://cezannec.github.io/Intro_Neural_Networks/
Image Classification in 10 Minutes with MNIST Dataset – https://towardsdatascience.com/image-classification-in-10-minutes-with-mnist-dataset-54c35b77a38d
Implement Transfer Learning with a generic Code Template – YouTube – https://www.youtube.com/watch?v=lSvo9mRrTHY&fbclid=IwAR3gUD4BdDp6NZPyfyZzdemQXoZsKALGM8gdqidHIPe2ICw1yhWzHEqTt5o
Convolutional Neural Networks Tutorial in TensorFlow – Adventures in Machine Learning – https://adventuresinmachinelearning.com/convolutional-neural-networks-tutorial-tensorflow/
Keras tutorial – build a convolutional neural network in 11 lines – Adventures in Machine Learning – https://adventuresinmachinelearning.com/keras-tutorial-cnn-11-lines/
Create your first Image Recognition Classifier using CNN, Keras and Tensorflow backend – https://medium.com/nybles/create-your-first-image-recognition-classifier-using-cnn-keras-and-tensorflow-backend-6eaab98d14dd
Series Introduction – Python Basics 1/10 – YouTube – https://www.youtube.com/watch?v=VQxBd5tLza8&list=PLlcnQQJK8SUjW_HiBWhZ_XOfCq9Hu0aeY&t=2s
How to Develop Competence With Deep Learning for Computer Vision – https://machinelearningmastery.com/how-to-develop-and-demonstrate-competence-with-deep-learning-for-computer-vision/
Intermediate Topics in Neural Networks – Towards Data Science – https://towardsdatascience.com/comprehensive-introduction-to-neural-network-architecture-c08c6d8e5d98
How to rapidly test dozens of deep learning models in Python – https://towardsdatascience.com/how-to-rapidly-test-dozens-of-deep-learning-models-in-python-cb839b518531
Advanced Topics in Deep Convolutional Neural Networks – https://towardsdatascience.com/advanced-topics-in-deep-convolutional-neural-networks-71ef1190522d
Applied Deep Learning – Part 4: Convolutional Neural Networks – https://towardsdatascience.com/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2
Best Way to Learn Python (Step-by-Step Guide) – Simpliv LLC – https://simpliv.wordpress.com/2019/06/27/best-way-to-learn-python-step-by-step-guide/
1. Intro – Quantyca – Medium – https://medium.com/quantyca/chepizzah-ai-an-ai-based-pizza-detector-176eb6dfed80
Convolutional Neural Network Tutorial (CNN) | Convolutional Neural Networks With TensorFlow – YouTube – https://www.youtube.com/watch?v=Xr3S-3vpPcU&t=1s&fbclid=IwAR0Hjx2CYTa2JfiZzRrMXXHnxOQ6tLMXwlWnX8LlCMexnRZdrefQuZbN1iE
Where We See Shapes, AI Sees Textures | Quanta Magazine – https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/?fbclid=IwAR3xdUQKteBRc3FnJnOpyEBlwnSNfaFmsRLcgcfA2WL0djVzWEyafdzPWoE
Deep Learning For Beginners Using Transfer Learning In Keras – https://towardsdatascience.com/keras-transfer-learning-for-beginners-6c9b8b7143e
Deep Learning from the Foundations · fast.ai – https://www.fast.ai/2019/06/28/course-p2v3/
Teach Yourself Programming in Ten Years – http://norvig.com/21-days.html
Exploring Neurons || Transfer Learning in Keras for custom data – VGG-16 – YouTube – https://www.youtube.com/watch?v=L7qjQu2ry2Q
CS231n Convolutional Neural Networks for Visual Recognition – http://cs231n.github.io/transfer-learning/
victorzhou.com – https://victorzhou.com/
CNNs, Part 1: An Introduction to Convolutional Neural Networks – victorzhou.com – https://victorzhou.com/blog/intro-to-cnns-part-1/
A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning – https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
dipanjanS/hands-on-transfer-learning-with-python: Deep learning simplified by transferring prior learning using the Python deep learning ecosystem – https://github.com/dipanjanS/hands-on-transfer-learning-with-python
How to Install TensorFlow GPU on Windows – FULL TUTORIAL – YouTube – https://www.youtube.com/watch?v=KZFn0dvPZUQ
How to fine-tune ResNet in Keras and use it in an iOS App via Core ML – https://heartbeat.fritz.ai/how-to-fine-tune-resnet-in-keras-and-use-it-in-an-ios-app-via-core-ml-ee7fd84c1b26
Here’s how you can accelerate your Data Science on GPU – https://towardsdatascience.com/heres-how-you-can-accelerate-your-data-science-on-gpu-4ecf99db3430
Transfer Learning for Image Classification using Keras – https://towardsdatascience.com/transfer-learning-for-image-classification-using-keras-c47ccf09c8c8
Rules-of-thumb for building a Neural Network – Towards Data Science – https://towardsdatascience.com/17-rules-of-thumb-for-building-a-neural-network-93356f9930af
Stylizing Video by Example – https://dcgi.fel.cvut.cz/home/sykorad/ebsynth.html
Introducing tf-explain, Interpretability for TensorFlow 2.0 – https://blog.sicara.com/tf-explain-interpretability-tensorflow-2-9438b5846e35
Stanford DAWN Deep Learning Benchmark (DAWNBench) · – https://dawn.cs.stanford.edu/benchmark/#imagenet-train-time
mlperf/inference: Reference implementations of inference benchmarks – https://github.com/mlperf/inference
Towards a metric for the energy efficiency of computer servers – ScienceDirect – https://www.sciencedirect.com/science/article/abs/pii/S0920548916301830
NVIDIA Boosts AI Performance in MLPerf v0.6 | NVIDIA Developer Blog – https://devblogs.nvidia.com/nvidia-boosts-ai-performance-mlperf-0-6/
Convenient Power Measurements on the Jetson TX2/Tegra X2 Board | Notes on Running DNNs on Embedded Platforms – https://embeddeddl.wordpress.com/2018/04/25/convenient-power-measurements-on-the-jetson-tx2-tegra-x2-board/
Object Detection on NVIDIA Jetson TX2 – Data Driven Investor – Medium – https://medium.com/datadriveninvestor/object-detection-on-nvidia-jetson-tx2-6090dc3e0595
NVIDIA Jetson TX2 Delivers Twice the Intelligence to the Edge | NVIDIA Developer Blog – https://devblogs.nvidia.com/jetson-tx2-delivers-twice-intelligence-edge/#disqus_thread
An Easy Guide to Gauge Equivariant Convolutional Networks – https://towardsdatascience.com/an-easy-guide-to-gauge-equivariant-convolutional-networks-9366fb600b70
Metrics_ML_FastPath19 – https://snehilverma41.github.io/Metrics_ML_FastPath19.pdf
Setting benchmarks in machine learning – O’Reilly Media – https://www.oreilly.com/ideas/setting-benchmarks-in-machine-learning
Google, Nvidia tout advances in AI training with MLPerf benchmark results | ZDNet – https://www.zdnet.com/article/google-nvidia-tout-advances-in-ai-training-with-mlperf-benchmark-results/
MLPerf – https://mlperf.org/inference-overview/#overview
Nvidia and Google Post new MLPerf AI Training Results | CdrInfo.com – https://www.cdrinfo.com/d7/content/nvidia-and-google-post-new-mlperf-ai-training-results
Google and NVIDIA Break MLPerf Records – SyncedReview – Medium – https://medium.com/syncedreview/google-and-nvidia-break-mlperf-records-f1cdaed7fec4
Cloud TPU Pods break AI training records | Google Cloud Blog – https://cloud.google.com/blog/products/ai-machine-learning/cloud-tpu-pods-break-ai-training-records
Reading Between the MLPerf Lines – https://www.nextplatform.com/2018/12/14/reading-between-the-mlperf-lines/
6a0120a5580826970c0240a493e942200d-pi (1198×1847) – https://www.patentlyapple.com/.a/6a0120a5580826970c0240a493e942200d-pi
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) – https://www.analyticsvidhya.com/blog/2018/04/introduction-to-graph-theory-network-analysis-python-codes/
Build a Hardware-based Face Recognition System for $150 with the Nvidia Jetson Nano and Python – https://medium.com/@ageitgey/build-a-hardware-based-face-recognition-system-for-150-with-the-nvidia-jetson-nano-and-python-a25cb8c891fd
ieee Upcoming Conferences for Computer Science & Electronics – http://www.guide2research.com/conferences/ieee
SAMI 2020 – http://conf.uni-obuda.hu/sami2020/index.html
Neural networks: training with backpropagation. – https://www.jeremyjordan.me/neural-networks-training/
Cloud GPUs Tutorial (comparing & using) – YouTube – https://www.youtube.com/watch?v=qWGgK4IrH-s&fbclid=IwAR2t1V8ccpmyDy6EjQ1jQ5xtx8V0Y42oUeILXVKF-5wu1S9Wj4EIxe4sXiI&t=7s
Automate the Boring Stuff with Python Programming | Udemy – https://www.udemy.com/automate/learn/lecture/3465796#overview
Review: YOLOv3 — You Only Look Once (Object Detection) – https://towardsdatascience.com/review-yolov3-you-only-look-once-object-detection-eab75d7a1ba6
Image classification from scratch in keras. Beginner friendly, intermediate exciting and expert refreshing. – https://towardsdatascience.com/image-detection-from-scratch-in-keras-f314872006c9
A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning – https://towardsdatascience.com/a-comprehensive-hands-on-guide-to-transfer-learning-with-real-world-applications-in-deep-learning-212bf3b2f27a
Approach pre-trained deep learning models with caution – https://medium.com/comet-ml/approach-pre-trained-deep-learning-models-with-caution-9f0ff739010c
Kirk Kaiser – Birding with Python and Machine Learning – PyCon 2018 – YouTube – https://www.youtube.com/watch?v=938yg4udxSc
Building a Self Contained Deep Learning Camera in Python with NVIDIA Jetson – Make Art with Python – https://www.makeartwithpython.com/blog/rich-mans-deep-learning-camera/
Amazon.com : Orient Power 12.8V 75AH New DIY Solar Battery Option Most Safe Lithium Battery Type LiFePO4 : Garden & Outdoor – https://www.amazon.com/lithium-LiFePO4-Battery-Electric-Batteries/dp/B07TDMQHPC?th=1
Terasic – All FPGA Main Boards – http://www.terasic.com.tw/cgi-bin/page/archive.pl?Language=English&CategoryNo=13&List=Simple
Top 15 Evaluation Metrics for Machine Learning with Examples – https://www.machinelearningplus.com/machine-learning/evaluation-metrics-classification-models-r/
Metrics to Evaluate your Machine Learning Algorithm – https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234
MLPerf – https://mlperf.org/press#mlperf-inference-launched
BD-20180604-MLPerf.pdf – https://research.spec.org/fileadmin/user_upload/documents/wg_bd/BD-20180604-MLPerf.pdf
Stanford DAWN Deep Learning Benchmark (DAWNBench) · – https://dawn.cs.stanford.edu//benchmark/
Tutorial on Hardware Accelerators for Deep Neural Networks – http://eyeriss.mit.edu/tutorial.html
Nvidia, Google Tie in Second MLPerf Training ‚At-Scale‘ Round – https://www.hpcwire.com/2019/07/10/nvidia-google-tie-in-second-mlperf-training-at-scale-round/
Training Results – MLPerf – https://mlperf.org/training-results-0-6/
MLPerf-Bench – https://sites.google.com/g.harvard.edu/mlperf-bench/home
How Machine Learning Can Transform The Energy Industry – https://towardsdatascience.com/how-machine-learning-can-transform-the-energy-industry-caaa965e282a
1907.10597v3.pdf – https://arxiv.org/pdf/1907.10597v3.pdf
1906.02243.pdf – https://arxiv.org/pdf/1906.02243.pdf
tbd-iiswc18.pdf – http://www.cs.toronto.edu/~serailhydra/publications/tbd-iiswc18.pdf
Snehil_Metrics_for_Machine_Learning_Workload_Benchmarking.pdf – https://researcher.watson.ibm.com/researcher/files/us-ealtman/Snehil_Metrics_for_Machine_Learning_Workload_Benchmarking.pdf
1906.11879.pdf – https://arxiv.org/pdf/1906.11879.pdf
Boost your CNN image classifier performance with progressive resizing in Keras – https://towardsdatascience.com/boost-your-cnn-image-classifier-performance-with-progressive-resizing-in-keras-a7d96da06e20
[1801.04381] MobileNetV2: Inverted Residuals and Linear Bottlenecks – https://arxiv.org/abs/1801.04381
Why MobileNet and Its Variants (e.g. ShuffleNet) Are Fast – https://medium.com/@yu4u/why-mobilenet-and-its-variants-e-g-shufflenet-are-fast-1c7048b9618d
MDZ-Reader | Band | Nature | Nature – https://reader.digitale-sammlungen.de/de/fs1/object/display/bsb11367247_00083.html?zoom=0.8000000000000003
How fast is my model? – https://machinethink.net/blog/how-fast-is-my-model/
Komplettset 1×100 Watt Monokristallin 5-Busbars 20A Laderegler 12V / 24V Kabel | Solarsets / Komplettangebote | Solarmodule | www.solartronics.de – https://www.solartronics.de/solarmodule/solarsets-komplettangebote/komplettset-1×100-watt-monokristallin-5-busbars-20a-laderegler-12v/24v-kabel-1000100m20
Basic motion detection and tracking with Python and OpenCV – PyImageSearch – https://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/
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