Introduction to object detection on Raspberry Pi
-
Frank
- December 13, 2021
- AI
- edge AI
- GDS: Yes
- Google Developers
- Machine Learning for Raspberry Pi
- ML for Raspberry Pi
- Object Detection
- object detection model
- pr_pr: TensorFlow
- pre-trained ML model
- pre-trained object detection
- purpose: Educate
- Raspberry Pi
- series: Coding TensorFlow
- TensorFlow
- tensorflow developer
- tensorflow developers
- Tensorflow light
- TensorFlow Lite
- TF Lite
- TFX
- type: DevByte (deck cleanup 0-10min)
In the first episode of Machine Learning for Raspberry Pi, learn how to download a pre-trained TensorFlow Lite object detection model and deploy it to your Raspberry Pi. This model can be used to recognize general objects such as an apple, a television, or a car.
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How to make TensorFlow models run faster on GPUs
XLA compilation on GPU can greatly boost the performance of your models, Learn how to use @tf.function(jit_compile=True) in TensorFlow to control what exact scopes are being compiled, and how to debug the performance of the resulting program. We’ll cover writing compiled models, debugging them, and exploring the performance characteristics and optimizations the XLA compiler performs, […]
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Intuitive Intro To Generative Adversarial Networks – GAN Course Preview
-
Frank
- July 26, 2021
- activation function
- AI
- artificial intelligence
- artificial neural network
- Autoencoders
- batch normalization
- CNN
- Convolutional Neural Network
- CUDA
- cuDNN
- data augmentation
- Deep Learning
- Education
- fine-tune
- GANS
- generative adversarial networks
- GPU
- image classification
- Keras
- Learning
- Machine Learning
- neural net
- Neural Network
- Nvidia
- Python
- PyTorch
- relu
- SGD
- Supervised Learning
- TensorFlow
- tensorflow.js
- train
- Training
- Transfer Learning
- Tutorial
- Unsupervised Learning
deeplizard has a brand new premium course, an Introduction to Generative Adversarial Networks (GANs). Below is the full first lesson of the course available as a preview to the course at large.
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Intro to Generative Adversarial Networks (GANs) – New Course Release
-
Frank
- July 19, 2021
- activation function
- AI
- artificial intelligence
- artificial neural network
- Autoencoders
- batch normalization
- CNN
- Convolutional Neural Network
- CUDA
- cuDNN
- data augmentation
- Deep Learning
- Education
- fine-tune
- GANS
- generative adversarial networks
- GPU
- image classification
- Keras
- Learning
- Machine Learning
- neural net
- Neural Network
- Nvidia
- Python
- PyTorch
- relu
- SGD
- Supervised Learning
- TensorFlow
- tensorflow.js
- train
- Training
- Transfer Learning
- Tutorial
- Unsupervised Learning
deeplizard announces their brand new premium deeplizard course, an Introduction to Generative Adversarial Networks (GANs)
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Introducing Retiarii: A deep learning exploratory-training framework on NNI
Traditional deep learning frameworks such as TensorFlow and PyTorch support training on a single deep neural network (DNN) model, which involves computing the weights iteratively for the DNN model. Designing a DNN model for a task remains an experimental science and is typically a practice of deep learning model exploration. Retrofitting such exploratory-training into the […]
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Convolutions in Deep Learning – Interactive Demo App
-
Frank
- June 6, 2021
- activation function
- AI
- artificial intelligence
- artificial neural network
- Autoencoders
- batch normalization
- Clustering
- CNN
- Convolutional Neural Network
- CUDA
- cuDNN
- data augmentation
- Deep Learning
- Education
- fine-tune
- GPU
- image classification
- Keras
- Learning
- Machine Learning
- neural net
- Neural Network
- Nvidia
- Python
- PyTorch
- relu
- Sequential model
- SGD
- Supervised Learning
- TensorFlow
- tensorflow.js
- TFJS
- train
- Training
- Transfer Learning
- Tutorial
- Unsupervised Learning
deeplizard explains the importance of convolutions in deep learning. In deep learning, convolution operations are the key components used in convolutional neural networks. A convolution operation maps an input to an output using a filter and a sliding window. Use the interactive demonstration below to gain a better understanding of this process.
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Use ML.NET to Score a Tensorflow Text Classification Model
Jon Wood recently streamed how to go over how to use a pre-trained TensorFlow model to predict sentiment within ML.NET.
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Intelligent AI Chatbot in Python
In today’s video, NeuralNine going to build an intelligent AI chatbot using neural networks and natural language processing in Python.
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Quantum Computing Basics – Creating Qubits
YouTuber BharatiDWConsultancy provides a quick overview of quantum computing and its fundamental building block: the qubit!
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Data Augmentation with TensorFlow’s Keras API
-
Frank
- October 14, 2020
- activation function
- AI
- artificial intelligence
- artificial neural network
- Autoencoders
- batch normalization
- Clustering
- CNN
- Convolutional Neural Network
- CUDA
- cuDNN
- data augmentation
- Deep Learning
- Education
- fine-tune
- GPU
- image classification
- Keras
- Learning
- Machine Learning
- neural net
- Neural Network
- Nvidia
- Python
- PyTorch
- relu
- Sequential model
- SGD
- Supervised Learning
- TensorFlow
- tensorflow.js
- TFJS
- train
- Training
- Transfer Learning
- Tutorial
- Unsupervised Learning
deeplizard demonstrates how to use data augmentation on images using TensorFlow’s Keras API. VIDEO SECTIONS 00:00 Welcome to DEEPLIZARD – Go to deeplizard.com for learning resources 00:17 Introduction to Data Augmentation 01:32 Image Augmentation with Keras 08:16 Collective Intelligence and the DEEPLIZARD HIVEMIND
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