#timeseries #tensorflow #guide
https://towardsdatascience.com/demystifying-tensorflow-time-series-local-linear-trend-9bec0802b24a
https://towardsdatascience.com/demystifying-tensorflow-time-series-local-linear-trend-9bec0802b24a
Medium
Demystifying Tensorflow Time Series: Local Linear Trend
Learn how Tensorflow uses linear dynamical system, Kalman filter and variational inference to model time series and make predictions.
#ml #dl #nn #tools #tensorflow #python
https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074
https://hackernoon.com/everything-you-need-to-know-about-tensorflow-2-0-b0856960c074
Hackernoon
Everything you need to know about TensorFlow 2.0 | HackerNoon
On June 26 of 2019, I will be giving a TensorFlow (TF) 2.0 workshop at the <a href="https://www.papis.io/latam-2019">PAPIs.io LATAM conference in SΓ£o Paulo</a>. Aside from the happiness of being representing <a href="https://www.daitan.com/">Daitan</a> asβ¦
#dl #nn #tools #tensorflow #horovod #training #distributes #guide
https://towardsdatascience.com/a-quick-guide-to-distributed-training-with-tensorflow-and-horovod-on-amazon-sagemaker-dae18371ef6e
https://towardsdatascience.com/a-quick-guide-to-distributed-training-with-tensorflow-and-horovod-on-amazon-sagemaker-dae18371ef6e
Medium
A quick guide to distributed training with TensorFlow and Horovod on Amazon SageMaker
Learn how distributed training works and how Amazon SageMaker makes it as easy as training on your laptop
#dl #nn #architectures #resnet #keras #tensorflow #tuning #guide
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
PyImageSearch
Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to fine-tune ResNet using Keras, TensorFlow, and Deep Learning.
#ds #nn #keras #tensorflow #autoencoders #guide
https://www.pyimagesearch.com/2020/02/17/autoencoders-with-keras-tensorflow-and-deep-learning/
https://www.pyimagesearch.com/2020/02/17/autoencoders-with-keras-tensorflow-and-deep-learning/
PyImageSearch
Autoencoders with Keras, TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to implement and train autoencoders using Keras, TensorFlow, and Deep Learning.
#dl #nn #tensorflow #audio #videos #tutorial
https://www.youtube.com/playlist?list=PL-wATfeyAMNrtbkCNsLcpoAyBBRJZVlnf
https://www.youtube.com/playlist?list=PL-wATfeyAMNrtbkCNsLcpoAyBBRJZVlnf
YouTube
Deep Learning (for Audio) with Python
In this series, I explore theory and implementation of deep learning in the Python programming language. The course focuses on applications of deep learning ...
#dl #nn #tensorflow #embedded #microcontrollers #guide #beginers
https://medium.com/@dmytro.korablyov/first-steps-with-esp32-and-tensorflow-lite-for-microcontrollers-c2d8e238accf
https://medium.com/@dmytro.korablyov/first-steps-with-esp32-and-tensorflow-lite-for-microcontrollers-c2d8e238accf
Medium
First steps with ESP32 and TensorFlow Lite for Microcontrollers
A story about my humble experience of creating a simple ML application with TensorFlow Lite for Microcontrollers on ESP32 platform.
#ml #dl #nn #cv #object_detection #opencv #keras #tensorflow #tutorial #guide
https://www.pyimagesearch.com/2020/06/22/turning-any-cnn-image-classifier-into-an-object-detector-with-keras-tensorflow-and-opencv
https://www.pyimagesearch.com/2020/06/29/opencv-selective-search-for-object-detection
https://www.pyimagesearch.com/2020/07/06/region-proposal-object-detection-with-opencv-keras-and-tensorflow
https://www.pyimagesearch.com/2020/07/13/r-cnn-object-detection-with-keras-tensorflow-and-deep-learning
https://www.pyimagesearch.com/2020/06/22/turning-any-cnn-image-classifier-into-an-object-detector-with-keras-tensorflow-and-opencv
https://www.pyimagesearch.com/2020/06/29/opencv-selective-search-for-object-detection
https://www.pyimagesearch.com/2020/07/06/region-proposal-object-detection-with-opencv-keras-and-tensorflow
https://www.pyimagesearch.com/2020/07/13/r-cnn-object-detection-with-keras-tensorflow-and-deep-learning
PyImageSearch
Turning any CNN image classifier into an object detector with Keras, TensorFlow, and OpenCV - PyImageSearch
In this tutorial, you will learn how to take any pre-trained deep learning image classifier and turn it into an object detector using Keras, TensorFlow, and OpenCV.
#ml #nn #dl #keras #tensorflow #cv #classification #multi_class #guide #tutorial
https://www.pyimagesearch.com/2020/10/12/multi-class-object-detection-and-bounding-box-regression-with-keras-tensorflow-and-deep-learning/
https://www.pyimagesearch.com/2020/10/12/multi-class-object-detection-and-bounding-box-regression-with-keras-tensorflow-and-deep-learning/
PyImageSearch
Multi-class object detection and bounding box regression with Keras, TensorFlow, and Deep Learning - PyImageSearch
In this tutorial, you will learn how to train a custom multi-class object detector using bounding box regression with the Keras and TensorFlow deep learning libraries. Last weekβs tutorial covered how to train single-class object detector using bounding boxβ¦
#ml #dl #cv #keras #tensorflow #tutorial #siamese_network
https://www.pyimagesearch.com/2020/11/30/siamese-networks-with-keras-tensorflow-and-deep-learning/
https://www.pyimagesearch.com/2020/11/23/building-image-pairs-for-siamese-networks-with-python/
https://www.pyimagesearch.com/2020/12/07/comparing-images-for-similarity-using-siamese-networks-keras-and-tensorflow/
https://www.pyimagesearch.com/2020/11/30/siamese-networks-with-keras-tensorflow-and-deep-learning/
https://www.pyimagesearch.com/2020/11/23/building-image-pairs-for-siamese-networks-with-python/
https://www.pyimagesearch.com/2020/12/07/comparing-images-for-similarity-using-siamese-networks-keras-and-tensorflow/
PyImageSearch
Siamese network with Keras, TensorFlow, and Deep Learning - PyImageSearch
In this tutorial you will learn how to implement and train a siamese network using Keras, TensorFlow, and Deep Learning.