Neural Network Layers – Deep Learning Dictionary
-
Frank
- May 16, 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
What are the layers that make up an artificial neural network?
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Deep Learning with TensorFlow – Course Reflection
-
Frank
- May 9, 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
Here’s the final video on DeepLizard’s Deep Learning with TensorFlow course.
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Computer Vision – Deep Learning Dictionary
-
Frank
- March 9, 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
What is computer vision? What terms and technologies are involved? Fortunately, this Deep Lizard video covers the basics.
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Narrow AI vs. AGI – Deep Learning Dictionary
-
Frank
- February 14, 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
What is narrow artificial intelligence, and how is it different from artificial general intelligence?
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Artificial Intelligence (AI) – Deep Learning Dictionary
-
Frank
- February 8, 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
What is artificial intelligence (AI) exactly?
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Deep Learning Dictionary – Course Introduction
-
Frank
- January 31, 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
This is a lightweight crash course containing bite-sized, practical and intuitive explanations for the most common terms and concepts in the field of deep learning. I’ve always admired the work that the Deep Lizard team has been doing.
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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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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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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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Fine-Tuning MobileNet on Custom Dataset with TensorFlow’s Keras API
-
Frank
- September 28, 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
- train
- Training
- Transfer Learning
- Tutorial
- Unsupervised Learning
In this episode, Mandy from deeplizard will be building on what we’ve learned about MobileNet combined with the techniques we’ve used for fine-tuning to fine-tune MobileNet for a custom image data set using TensorFlow’s Keras API.
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