AI vs. Machine Learning vs. Deep Learning – Relationship Overview
-
Frank
- January 24, 2022
- 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
In this lesson, begin your deep learning journey by understanding where the field of deep learning falls in relation to the fields of machine learning and artificial intelligence (AI).
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PyTorch and Monai for AI Healthcare Imaging – Python Machine Learning Course
Learn how to use PyTorch, Monai, and Python for computer vision using machine learning. One practical use-case for artificial intelligence is healthcare imaging. In this course, you will improve your machine learning skills by creating an algorithm for automatic liver segmentation. Code: https://github.com/amine0110/Liver-Segmentation-Using-Monai-and-PyTorch Course Contents: (0:00:00) Introduction (0:02:11) What is U-Net (0:13:21) Software Installation (0:22:35) […]
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Machine Learning Databases and How to Access them with Pytorch – MNIST Tutorial
Here is a great tutorial about popular Machine Learning databases and how we can easily access them with Pytorch. In particular, we will focus on MNIST, which is a handwritten digits database with 70,000 different images. We will load it with a very simple Pytorch command and we will have a closer look at its […]
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5 Ways to Increase Your Model Performance with PyTorch Profiler
Sabrina Smai shows us five ways to increase your model performance using PyTorch Profiler.
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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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BlazorDay 2021
On my livestream today, I briefly mentioned that I was excited about the potential of Blazor for web development. Fortunately, The Blazor Day is the online event around Blazor technologies. It’s organized by organized by 3 MVPs and it looks awesome.
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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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MLflow Integration with PyCaret and PyTorch
In this talk, hosted by Databricks, learn how to build reproducible AI models and workflows using PyTorch and MLflow that can be shared across your teams, with traceability and speed up collaboration for AI projects.
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