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First of all, let’s look at the topics that we will cover in this blog:
TensorFlow is one of the most in-demand tools used by ML/AI Engineers. It is an open-source framework developed by Google, which is used to build various Machine Learning and Deep Learning models.
TensorFlow helps us train and execute neural network image recognition, natural language processing, digit classification, and many more. Also, using the same models used for development, TensorFlow facilitates the estimation of the output at various scales.
The main objective of using TensorFlow is not just the development of a deep neural network. But, it is focused to reduce the complexity of implementing computations on large numerical datasets. Since Deep Learning models require a lot of computation for attaining accuracy, companies started using TensorFlow. Thus, Google made TensorFlow available to everyone.
#tensorflow #deep-learning #data-science #python #developer
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Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!
2. Do the course questions in parallel in PyCharm.
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#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing
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In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.
We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.
#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2
1606549375
First of all, let’s look at the topics that we will cover in this blog:
TensorFlow is one of the most in-demand tools used by ML/AI Engineers. It is an open-source framework developed by Google, which is used to build various Machine Learning and Deep Learning models.
TensorFlow helps us train and execute neural network image recognition, natural language processing, digit classification, and many more. Also, using the same models used for development, TensorFlow facilitates the estimation of the output at various scales.
The main objective of using TensorFlow is not just the development of a deep neural network. But, it is focused to reduce the complexity of implementing computations on large numerical datasets. Since Deep Learning models require a lot of computation for attaining accuracy, companies started using TensorFlow. Thus, Google made TensorFlow available to everyone.
#tensorflow #deep-learning #data-science #python #developer
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I have not created the Object Detection model, I have just merely cloned Google’s Tensor Flow Lite model and followed their Raspberry Pi Tutorial which they talked about in the Readme! You don’t need to use this article if you understand everything from the Readme. I merely talk about what I did!
I have used a Raspberry Pi 3 Model B and PI Camera Board (3D printed a case for camera board). **I had this connected before starting and did not include this in the 90 minutes **(plenty of YouTube videos showing how to do this depending on what Pi model you have. I used a video like this a while ago!)
I have used my Apple Macbook which is Linux at heart and so is the Raspberry Pi. By using Apple you don’t need to install any applications to interact with the Raspberry Pi, but on Windows you do (I will explain where to go in the article if you use windows)
#raspberry-pi #object-detection #raspberry-pi-camera #tensorflow-lite #tensorflow #tensorflow lite object detection using raspberry pi and pi camera
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This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) method. The representation of each face will be an Guassian distribution parametrized by (mu, sigma), where mu is the original embedding and sigma is the learned uncertainty. Experiments show that ProbFace could
#machine learning #tensorflow #testing #a demo code of training and testing using tensorflow #a demo code of training #testing using tensorflow