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Multiprocessing with OpenCV and Python
In this tutorial, you will learn how to use multiprocessing with OpenCV and Python to perform feature extraction. You’ll learn how to use multiprocessing with OpenCV to parallelize feature extraction...
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Install OpenCV 4 on Raspberry Pi 4 and Raspbian Buster
In this tutorial you will learn how to install OpenCV 4 on the Raspberry Pi 4 and Raspbian Buster. You will learn how to install OpenCV 4 on Raspbian Buster via both: A simple pip-install method...
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Keras: Starting, stopping, and resuming training
In this tutorial, you will learn how to use Keras to train a neural network, stop training, update your learning rate, and then resume training from where you left off using the new learning rate....
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Rectified Adam (RAdam) optimizer with Keras
In this tutorial, you will learn how to use Keras and the Rectified Adam optimizer as a drop-in replacement for the standard Adam optimizer, potentially leading to a higher accuracy model (and in...
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Is Rectified Adam actually *better* than Adam?
Is the Rectified Adam (RAdam) optimizer actually better than the standard Adam optimizer? According to my 24 experiments, the answer is no, typically not (but there are cases where you do want to use...
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Why is my validation loss lower than my training loss?
In this tutorial, you will learn the three primary reasons your validation loss may be lower than your training loss when training your own custom deep neural networks. I first became interested in...
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Keras vs. tf.keras: What’s the difference in TensorFlow 2.0?
In this tutorial you’ll discover the difference between Keras and tf.keras , including what’s new in TensorFlow 2.0. Today’s tutorial is inspired from an email I received last Tuesday from...
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3 ways to create a Keras model with TensorFlow 2.0 (Sequential, Functional,...
Keras and TensorFlow 2.0 provide you with three methods to implement your own neural network architectures: Sequential API Functional API Model subclassing Inside of this tutorial you’ll learn how to...
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Traffic Sign Classification with Keras and Deep Learning
In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning. Last weekend I drove down to Maryland to...
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Detecting Natural Disasters with Keras and Deep Learning
In this tutorial, you will learn how to automatically detect natural disasters (earthquakes, floods, wildfires, cyclones/hurricanes) with up to 95% accuracy using Keras, Computer Vision, and Deep...
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Fire and smoke detection with Keras and Deep Learning
In this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library. Today’s tutorial is inspired by an email I received last week from...
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An interview with Paul Lee – Doctor, Cardiologist and Deep Learning Researcher
In today’s blog post, I interview Dr. Paul Lee, a PyImageSearch reader and interventional cardiologist affiliated with NY Mount Sinai School of Medicine. Dr. Lee recently presented his research at the...
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Human Activity Recognition with OpenCV and Deep Learning
In this tutorial you will learn how to perform Human Activity Recognition with OpenCV and Deep Learning. Our human activity recognition model can recognize over 400 activities with 78.4-94.5% accuracy...
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OpenCV Vehicle Detection, Tracking, and Speed Estimation
In this tutorial, you will learn how to use OpenCV and Deep Learning to detect vehicles in video streams, track them, and apply speed estimation to detect the MPH/KPH of the moving vehicle. This...
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How to install TensorFlow 2.0 on macOS
In this tutorial, you will learn to install TensorFlow 2.0 on your macOS system running either Catalina or Mojave There are a number of important updates in TensorFlow 2.0, including eager execution,...
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How to install TensorFlow 2.0 on Ubuntu
In this tutorial, you will learn to install TensorFlow 2.0 on your Ubuntu system either with or without a GPU. There are a number of important updates in TensorFlow 2.0, including eager execution,...
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Training a custom dlib shape predictor
In this tutorial, you will learn how to train your own custom dlib shape predictor. You’ll then learn how to take your trained dlib shape predictor and use it to predict landmarks on input images and...
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Tuning dlib shape predictor hyperparameters to balance speed, accuracy, and...
In this tutorial, you will learn how to optimally tune dlib’s shape predictor hyperparameters and options to obtain a shape predictor that balances speed, accuracy, and model size. Today is part two...
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Label smoothing with Keras, TensorFlow, and Deep Learning
In this tutorial, you will learn two ways to implement label smoothing using Keras, TensorFlow, and Deep Learning. When training your own custom deep neural networks there are two critical questions...
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Raspberry Pi and Movidius NCS Face Recognition
In this tutorial you will learn how to use the Movidius NCS to speedup face detection and face recognition on the Raspberry Pi by over 243%! If you’ve ever tried to perform deep learning-based face...
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