ποΈ Evaluating DeepFace
An introduction to DeepFace Face Recognition with Tensorflow.
ποΈ Vector Databases for AI Applications
An introduction to Weaviate DB for end-to-end AI applications.
ποΈ DLIB Face Recognition
Detect faces in images and compare their feature vector to known entities
ποΈ Audio Classification with Computer Vision
Training an YOLOv8 Classifier to Identify Audio Files
ποΈ CVAT Semi-automatic and Automatic Annotation
CVAT supports supervised machine learning tasks pertaining to object detection, image classification, image segmentation and 3D data annotation.
ποΈ Computer Vision Annotation Tool (CVAT) Introduction
CVAT supports supervised machine learning tasks pertaining to object detection, image classification, image segmentation and 3D data annotation.
ποΈ YOLOv8 Nightshift
Training an YOLOv8 Object Tracker for Day/Night Cameras
ποΈ YOLOv8 License Plate Detection
Using the YOLOv8 Object Tracker in Combination with EasyOCR
ποΈ Scikit-Learn ML Model Explainability
SHAP is a game theoretic approach to explain the output of any machine learning model.
ποΈ Using Tensorflow Models in OpenCV
Using Tensorflow models in OpenCV to perform semantic segmentations on images using the Mask-RCNN model.
ποΈ YOLOv8 Image Classifier
I-know-flowers Image Classifier YOLOv8 Model Comparison
ποΈ Detectron Object Detection with OpenImages Dataset (WIP)
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
ποΈ Instance Segmentation with PyTorch (Mask RCNN)
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
ποΈ Image Segmentation with PyTorch (Faster RCNN)
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
ποΈ Image Segmentation with PyTorch (RCNN)
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
ποΈ Image Segmentation with PyTorch
Food item segmentation from images of the Tray Food Segmentation dataset
ποΈ Containerized PyTorch Dev Workflow
Develop your PyTorch models inside the official PyTorch container image.
ποΈ Tensorflow Image Classifier - Model Evaluation
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - Xception
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - ViT
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - NASNetMobile
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - MobileNetV3Small
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - MobileNetV3Large
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - MobileNetV2
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - InceptionV3
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - EfficientNetV2S
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - EfficientNetV2B0
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - Data-efficient Image Transformers
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - Data Pre-processing
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow Image Classifier - Introduction
Blue print image classifier using Tensorflow and Keras Applications pre-trained models
ποΈ Tensorflow VITs
ViT models apply the Transformer architecture with self-attention to sequences of image patches, without using convolution layers.
ποΈ Human Emotion Detection with Tensorflow
Create a TF Image Classifier that can distinguish between different human emotion based on portrait photos.
ποΈ Working with ONNX Models
Open Neural Network Exchange (ONNX)
ποΈ Introduction to Caffe2
Deep Learning Framework with Python for flexibility and C++ for speed.
ποΈ SQL in Data Science - Machine Learning
Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases
ποΈ SQL in Data Science - Slightly more Advanced Queries
Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases
ποΈ SQL in Data Science - The Basics using Python
Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases
ποΈ Detection of Exoplanets using Transit Photometry
Exoplanets are the planets found outside of the solar system. When a planet passes in front of a star, the brightness of that star as observed by us becomes dimmer depending on the size of the planet. The data we observe will show a dip in flux if a planet is transiting the star we are observing.
ποΈ (Re) Introduction to Tensorflow Natural Language Processing
Using Tensorflow to classify Disaster Tweet.
ποΈ 3D Image Classification
Learn how to train a 3D convolutional neural network (3D CNN) to predict presence of pneumonia.
ποΈ Dimensionality Reduction for Image Segmentation
Use Manifold Learning and the LD Analysis to Visualize Image Datasets.
ποΈ Fisher Linear Discriminant Analysis (LDA)
LDA is a widely used dimensionality reduction technique built on Fisherβs linear discriminant.
ποΈ Isometric Mapping (ISOMAP)
Non-linear dimensionality reduction through Isometric Mapping
ποΈ Multidimensional Scaling (MDS)
Multidimensional Scaling is a family of statistical methods that focus on creating mappings of items based on distance.
ποΈ tStochastic Neighbor Embedding (t-SNE)
t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.
ποΈ Locally Linear Embedding (LLE)
LLE is an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data.
ποΈ Principal Component Analysis (PCA)
To aid visualization of the structure of a dataset, the dimension must be reduced in some way.
ποΈ Tensorflow 2 - Unsupervised Learning
Generative Adverserial Networks for Image Data Generation
ποΈ Tensorflow 2 - Unsupervised Learning
Use Autoencoders to Increase Feature Resolution
ποΈ Tensorflow 2 - Unsupervised Learning
Use Autoencoders to Reduce Dimensionality and Feature Discovery
ποΈ Tensorflow 2 - Transfer Learning
Scale a pre-trained model to fit your needs
ποΈ Tensorflow 2 - Transfer Learning
Fine-tuning Pre-trained Models
ποΈ Tensorflow 2 - Transfer Learning
Using a Pre-trained Model to Extract Features
ποΈ Tensorflow 2 - Convolutional Neural Networks
Computer Vision for Multiclass Image Classifications
ποΈ Tensorflow 2 - Convolutional Neural Networks
Computer Vision for Binary Image Classifications
ποΈ Tensorflow 2 - Neural Network Classifications
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ποΈ Tensorflow 2 - Neural Network Classification
Model Evaluation and Performance Improvement
ποΈ Tensorflow 2 - Neural Network Classification
Working with a non-linear dataset and activation functions
ποΈ Tensorflow 2 - Neural Network Regression
Data pre-processing - normalization and feature-scaling
ποΈ Tensorflow 2 - Neural Network Regression
Working with the medical cost dataset
ποΈ Tensorflow 2 - Neural Network Regression
Optimizing model performance
ποΈ Tensorflow 2 - Neural Network Regression
Visualizing Models and Evaluating Model Performance
ποΈ Tensorflow 2 - Neural Network Regression
Building a Regression Model and Improving it's Performance
ποΈ Tensorflow 2 - An (Re)Introduction 2023 (3)
Matrix multiplications, Squeeze, One-hot and Numpy
ποΈ Tensorflow 2 - An (Re)Introduction 2023 (2)
Tensor Indexing, Expanding and Manipulations
ποΈ Tensorflow 2 - An (Re)Introduction 2023
Tensor Constants, Variables and Attributes
ποΈ Keras for Tensorflow - VGG16 Network Architecture
An example convolutional neural network is the VGG16 Architecture.
ποΈ Keras for Tensorflow - Recurrent Neural Networks
Recurrent Neural Networks are widely used to work with sequence data such as time series or natural language.
ποΈ Keras for Tensorflow - Convolutional Neural Networks
Convolutional Neural Networks are ideal for Computer Vision tasks.
ποΈ Keras for Tensorflow - Artificial Neural Networks
Building a deep neural network using the MNIST dataset.
ποΈ YOLOv8 with AS-One
A Modular Libary for YOLO Object Detection and Object Tracking.
ποΈ Keras for Tensorflow - An (Re)Introduction 2023
Keras is built on top of TensorFlow 2 and provides an API designed for human beings.
ποΈ SciKit Wine Quality
Predicting Wine Quality with Several Classification Techniques using SciKit Learn.
ποΈ OpenCV Count My Money
Differentiate Objects based on their Contour and Colour with cvZone.
ποΈ YOLOv7 to Tensorflow
Converting a YOLOv7 PyTorch Model to Tensorflow (Lite)
ποΈ YOLOv7 Label Conversion
Transferring PASCAL VOC labels to the YOLO format
ποΈ YOLOv7 Training with Custom Data
Use your Custom Dataset to train YOLOv7
ποΈ MiDaS Depth Vision
MiDaS computes relative inverse depth from a single image.
ποΈ YOLOv7 Introduction
Getting started with object detection in YOLOv7
ποΈ Recurrent Neural Networks
Generate text using a character-based RNN
ποΈ Deep Convolutional Generative Adversarial Network
Use Adversarial Networks to generate Images
ποΈ Tensorflow Downsampling
Using Representation Learning to Downsample Images
ποΈ Tensorflow Deep Dream
DeepDream is an experiment that visualizes the patterns learned by a neural network.
ποΈ Tensorflow Representation Learning
Build a Denoising Autoencoder in Tensorflow using the mnist Digits Dataset
ποΈ Tensorflow Hub
TensorFlow Hub is a repository of trained machine learning models.
ποΈ Tensorflow Transfer Learning
Transfer learning is a machine learning technique in which intelligence from a base ann is being transferred to a new network as a starting point.
ποΈ Tensorflow Image Classification
The CIFAR-10 is a labeled subset of the 80 million tiny images dataset that can be directly downloaded using Keras.
ποΈ Breast Histopathology Image Segmentation Part 6
Running Predictions
ποΈ Breast Histopathology Image Segmentation Part 5
Evaluate the performance of your trained model
ποΈ Breast Histopathology Image Segmentation Part 4
Train our model to fit the dataset
ποΈ Breast Histopathology Image Segmentation Part 3
Model creation based on a pre-trained and a custom model
ποΈ Breast Histopathology Image Segmentation Part 2
Balancing skewed datasets and data augmentation
ποΈ Breast Histopathology Image Segmentation Part 1
Data Inspection and Pre-processing
ποΈ Deep Docker on Arch
The NVIDIA Container Toolkit run GPU accelerated Docker containers
ποΈ Face Restoration with GFPGAN
Victoria Harbour, Hongkong
ποΈ Super Resolution with Real-ESRGAN
Victoria Harbour, Hongkong
ποΈ Super Resolution with ESRGAN
Victoria Harbour, Hongkong
ποΈ Deep Audio
Deep Audio Classifier with Tensorflow
ποΈ Yolo App - YOLOv5 Data Preparation
Shenzhen, China
ποΈ Yolo App - Flask Web Application
Shenzhen, China
ποΈ Yolo App - Tesseract Optical Character Recognition
Shenzhen, China
ποΈ Yolo App - Pipeline Predictions
Shenzhen, China
ποΈ Yolo App - Train a Model with Tensorflow
Shenzhen, China
ποΈ Yolo App - Data Collection
Shenzhen, China
ποΈ OpenCV Optical Flow Algorithm for Object Tracking
Shenzhen, China
ποΈ OpenCV CAMshift Algorithm for Object Tracking
Shenzhen, China
ποΈ OpenCV Meanshift Algorithm for Object Tracking
Shenzhen, China
ποΈ OpenCV Object Detection and Tracking
Shenzhen, China
ποΈ OpenCV Object Tracking
Shenzhen, China
ποΈ OpenCV Face Detection and Privacy
Shenzhen, China
ποΈ OpenCV Image Objects
Shenzhen, China
ποΈ OpenCV Image Operations
Shenzhen, China
ποΈ OpenCV, Streams and Video Files
Shenzhen, China
ποΈ OpenCV and Images
Shenzhen, China
ποΈ Introduction into FB Prophet
Shenzhen, China
ποΈ Tensorflow.js React App
Mong Kok, Hongkong
ποΈ Tensorflow2 Model Zoo
Mong Kok, Hongkong
ποΈ Tensorflow2 Crash Course - Part V
Mong Kok, Hongkong
ποΈ Tensorflow2 Crash Course - Part IV
Mong Kok, Hongkong
ποΈ Tensorflow2 Crash Course - Part III
Mong Kok, Hongkong
ποΈ Tensorflow2 Crash Course - Part II
Mong Kok, Hongkong
ποΈ Tensorflow Crash Course - Part I
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ποΈ OpenCV Crash Course Part II
Shenzhen, China
ποΈ OpenCV Crash Course Part I
Shenzhen, China
ποΈ License Plate Recognition with YOLOv4, OpenCV and Tesseract
Shenzhen, China
ποΈ Installing YOLOv4 with Anaconda
Shenzhen, China
ποΈ Streamlit user interface for openCV/Mediapipe face mesh app
Victoria Harbour, Hongkong
ποΈ spaCy NER Predictions
Victoria Harbour, Hongkong
ποΈ spaCy NER on Arch Linux
Victoria Harbour, Hongkong
ποΈ Introduction to Keras
Shanghai, China
ποΈ Tesseract OCR on Arch Linux
Victoria Harbour, Hongkong
ποΈ Introduction to TensorFlow 2 Beta
Shanghai, China
ποΈ Machine Learning with SciKit Learn
Shenzhen, China