Jonathan Hui blog

Nov 3, 2017 “Understanding Dynamic Routing between Capsules (Capsule Networks)”
“A simple tutorial in understanding Capsules, Dynamic routing and Capsule Network CapsNet”
Nov 14, 2017 “Understanding Matrix capsules with EM Routing (Based on Hinton's Capsule Networks)”
“A simple tutorial in understanding Matrix capsules with EM Routing in Capsule Networks”
Mar 5, 2017 “Generative adversarial nets (GAN) , DCGAN, CGAN, InfoGAN”
“Generative adversarial nets, improving GAN, DCGAN, CGAN, InfoGAN”
Mar 15, 2017 “Fast R-CNN and Faster R-CNN”
“Object detection using Fast R-CNN and Faster R-CNN.”
Mar 15, 2017 “RNN, LSTM and GRU tutorial”
“This tutorial covers the RNN, LSTM and GRU networks that are widely popular for deep learning in NLP.”
Sep 7, 2017 “TensorFlow - Install CUDA, CuDNN & TensorFlow in AWS EC2 P2”
“TensorFlow - Deploy TensorFlow application in AWS EC2 P2 with CUDA & CuDNN”
Mar 18, 2017 “Deep learning without going down the rabbit holes.”
“How to learn deep learning from easy concept to complex idea? How to build insight along the way?”
Mar 17, 2017 “Deep learning without going down the rabbit holes. (Part 2)”
“Part 2 of the deep learning.”
Mar 16, 2017 “Convolutional neural networks (CNN) tutorial”
“Convolutional networks explore features by discover its spatial information. This tutorial will build CNN networks for visual recognition.”
Mar 15, 2017 “Soft & hard attention”
“How to use attention to improve deep network learning? Attention extracts relevant information selectively for more effective training.”
Mar 6, 2017 “Class visualization, style transfer and DeepDream”
“Use a generative model to visualize or to transfer or exagerrate sytle.”
Mar 6, 2017 “Variational Autoencoders”
“Variational Autoencoders”
Apr 30, 2017 “DRAW - Deep recurrent attentive writer”
“A generative model to generate images using LSTM and attention.”
Mar 15, 2017 “Memory network (MemNN) & End to end memory network (MemN2N), Dynamic memory network”
“Use a memory network to store knowledge for inferencing.”
Mar 6, 2017 “Reinforcement learning”
“Reinforcement learning with deep learning”
Mar 6, 2017 “CUDA Tutorial”
“NVIDIA CUDA”
Feb 13, 2018 “TensorFlow Basic - tutorial.”
“TensorFlow is a very powerful platform for Machine Learning. This tutorial goes over some of the basic of TensorFlow.”
Mar 14, 2017 “TensorFlow Estimator”
“TensorFlow Estimator”
Mar 8, 2017 “TensorFlow variables, saving/restore”
“TensorFlow variables, saving/restore”
Mar 12, 2017 “TensorBoard - Visualize your learning.”
“TensorBoard make your machine learning visualization easy.”
Nov 21, 2017 “TensorFlow - Importing data”
“How to read data into the TensorFlow?”
Mar 7, 2017 “TensorFlow performance and advance topics”
“Cover TensorFlow advance topics including performance and other advance topics.”
Mar 7, 2017 “TensorFlow with multiple GPUs”
“TensorFlow operation placement on multiple GPUs.”
Feb 11, 2018 “Keras tutorial.”
“Keras tutorial.”
Feb 11, 2018 “How to start a Deep Learning project?”
“How to start and finish a Deep Learning project?”
Feb 9, 2018 “PyTorch - Basic operations”
“PyTorch - Basic operations”
Feb 9, 2018 “PyTorch - Variables, functionals and Autograd.”
“PyTorch - Variables, functionals and Autograd.”
Feb 9, 2018 “PyTorch - Neural networks with nn modules”
“PyTorch - Neural networks with nn modules”
Feb 9, 2018 “PyTorch - Data loading, preprocess, display and torchvision.”
“PyTorch - Data loading, preprocess, display and torchvision.”
Feb 9, 2018 “PyTorch - nn modules common APIs”
“PyTorch - nn modules common APIs”
Jan 15, 2017 “Machine learning - Deep learning project approach and resources”
“Machine learning - Deep learning project approach and resources.”
Jan 15, 2017 “Reading text with deep learning”
“Reading text with deep learning”
Jan 15, 2017 “Machine learning - Gaussian Process”
“Machine learning - Gaussian Process”
Jan 15, 2017 “Machine learning - Visualization, multi-dimensional scaling, Sammon mapping, IsoMap and t-sne”
“Machine learning - Visualization, Multi-dimensional scaling, Sammon mapping, IsoMap and t-sne”
Jan 15, 2017 “Machine learning - Regression, Logistic regression, SVM, MAP and Kernels”
“Machine learning - Regression, Logistic regression, SVM, MAP and Kernels”
Jan 15, 2017 “Machine learning - PCA, SVD, Matrix factorization and Latent factor model”
“Machine learning - PCA, SVD, Matrix factorization and Latent factor model”
Jan 15, 2017 “Machine learning - Clustering, Density based clustering and SOM”
“Machine learning - Clustering, Density based clustering and SOM”
Jan 15, 2017 “Machine learning - Recommendation, Collaborative filtering and ranking”
“Machine learning - Recommendation, Collaborative filtering, Low rank matrix factorization and ranking”
Jan 15, 2017 “Machine learning - Naive bayes classifier, Bayesian inference”
“Machine learning (Naive Bayes and Bayesian inference)”
Jan 15, 2017 “Machine learning - Anomaly detection”
“Machine learning - Anomaly detection”
Jan 15, 2017 “Machine learning - Hidden Markov Model (HMM)”
“Machine learning - Hidden Markov Model (HMM)”
Jan 15, 2017 “Machine learning - Decision tree, Random forest, Ensemble methods and Beam searach”
“Machine learning - Decision tree, Random forest, Ensemble methods and Beam searach”
Jan 15, 2017 “Machine learning - Nonsupervised and semi-supervised learning”
“Machine learning - Nonsupervised and semi-supervised learning”
Jan 15, 2017 “Machine learning - Notes”
“Machine learning - Notes”
Jan 15, 2017 “Apache Spark, Spark SQL, DataFrame, Dataset”
“Apache Spark, Spark SQL, DataFrame, Dataset”
Jan 15, 2017 “Apache Spark Structured Streaming”
“Apache Spark Structured Streaming”
Jan 15, 2017 “Apache Storm”
“Apache Storm”
Jan 15, 2017 “Apache Kafka”
“Apache Kafka”
Jan 15, 2017 “Machine learning - Restricted Boltzmann Machines.”
“Machine learning - Restricted Boltzmann Machines.”
Jan 5, 2017 “Deep learning - Probability & distribution.”
“Deep learning - Probability & distribution.”
Jan 5, 2017 “Deep learning - Linear algebra.”
“Deep learning - Linear algebra.”
Jan 5, 2017 “Deep learning - Computation & optimization.”
“Deep learning - Computation & optimization.”
Jan 5, 2017 “Deep learning - Information theory & Maximum likelihood.”
“Deep learning - Information theory & Maximum likelihood..”