@DeepGravity - A very cool intro to Keras and CNN.rar
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Download a very cool intro to #Keras and #CNNs
Syllabus:
Keras 1, What is Keras
Keras 2, Installations for #DeepLearning, #Anaconda, #Jupyter Notebook, #Tensorflow, Keras
Keras 3, #NeuralNetwork Regression Model with Keras
Keras 4, Breast Cancer Diagnosis with Neural Networks
Keras 5, Understanding #ConvolutionalNeuralNetworks, Making a Handwritten Digit Calculator
Watch more videos on the related YouTube channel
🔭 @DeepGravity
Syllabus:
Keras 1, What is Keras
Keras 2, Installations for #DeepLearning, #Anaconda, #Jupyter Notebook, #Tensorflow, Keras
Keras 3, #NeuralNetwork Regression Model with Keras
Keras 4, Breast Cancer Diagnosis with Neural Networks
Keras 5, Understanding #ConvolutionalNeuralNetworks, Making a Handwritten Digit Calculator
Watch more videos on the related YouTube channel
🔭 @DeepGravity
François #Chollet is the creator of #Keras, which is an open source #DeepLearning library that is designed to enable fast, user-friendly experimentation with #deepNeuralNetworks. It serves as an interface to several deep learning libraries, most popular of which is #TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class #AI researcher and software engineer at #Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of #ArtificialIntelligence. This conversation is part of the Artificial Intelligence podcast.
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DeepLearning Academy courses
Applied Deep Learning for Predictive Analytics
Deep Learning with #TensorFlow
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Applied Deep Learning for Predictive Analytics
Deep Learning with #TensorFlow
#DeepLearning
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Deeplearning-Academy
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Advanced Deep Learning Education and mentoring platform | Learn and practice on real Data Science projects | Get prepared to work as a Deep Learning Engineer.
Semantic Image #Segmentation with #DeepLab in #TensorFlow
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
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Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and mobile real-time video segmentation. Assigning these semantic labels requires pinpointing the outline of objects, and thus imposes much stricter localization accuracy requirements than other visual entity recognition tasks such as image-level classification or bounding box-level detection.
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Datasciencecentral
Semantic Image Segmentation with DeepLab in TensorFlow
This article was written by Liang-Chieh Chen and Yukun Zhu.
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person…
#TensorFlow 2 Tutorial: Get Started in #DeepLearning With tf.keras
After completing this tutorial, you will know:
The difference between Keras and tf.keras and how to install and confirm TensorFlow is working.
The 5-step life-cycle of tf.keras models and how to use the sequential and functional APIs.
How to develop MLP, CNN, and RNN models with tf.keras for regression, classification, and time series forecasting.
How to use the advanced features of the tf.keras API to inspect and diagnose your model.
How to improve the performance of your tf.keras model by reducing overfitting and accelerating training.
#Keras
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🔭 @DeepGravity
After completing this tutorial, you will know:
The difference between Keras and tf.keras and how to install and confirm TensorFlow is working.
The 5-step life-cycle of tf.keras models and how to use the sequential and functional APIs.
How to develop MLP, CNN, and RNN models with tf.keras for regression, classification, and time series forecasting.
How to use the advanced features of the tf.keras API to inspect and diagnose your model.
How to improve the performance of your tf.keras model by reducing overfitting and accelerating training.
#Keras
Link
🔭 @DeepGravity
Image Data Augmentation for #TensorFlow 2, #Keras and #PyTorch with Albumentations in #Python
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
TL;DR Learn how to create new examples for your dataset using image augmentation techniques. Load a scanned document image and apply various augmentations. Create an augmented dataset for Object Detection.
Article
🔭 @DeepGravity
Curiousily
Image Data Augmentation for TensorFlow 2, Keras and PyTorch with Albumentations in Python - Adventures in Artificial Intelligence…
Learn how to augment image data for Image Classification, Object Detection, and Image Segmentation
Automating Pac-man with #DeepQLearning: An Implementation in #Tensorflow.
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#DeepReinforcementLearning
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#DeepReinforcementLearning
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Medium
Automating Pac-man with Deep Q-learning: An Implementation in Tensorflow.
Fundamentals of Reinforcement Learning