162 docs tagged with "Machine Learning"

Using Tensorflow to classify Disaster Tweet.

Learn how to train a 3D convolutional neural network (3D CNN) to predict presence of pneumonia.

Airflow is a platform to author, schedule and monitor workflows.

Airflow is a platform to author, schedule and monitor workflows.

Airflow is a platform to author, schedule and monitor workflows.

Airflow is a platform to author, schedule and monitor workflows.

Training an YOLOv8 Classifier to Identify Audio Files

Using Amazon SageMaker / AutoGluon to find your perfect model fit.

Using Amazon SageMaker / AutoGluon to find your perfect model fit.

Using Amazon SageMaker / AutoGluon to find your perfect model fit.

Data Inspection and Pre-processing

Balancing skewed datasets and data augmentation

Model creation based on a pre-trained and a custom model

Train our model to fit the dataset

Evaluate the performance of your trained model

Running Predictions

CVAT supports supervised machine learning tasks pertaining to object detection, image classification, image segmentation and 3D data annotation.

Develop your PyTorch models inside the official PyTorch container image.

CVAT supports supervised machine learning tasks pertaining to object detection, image classification, image segmentation and 3D data annotation.

Open-source Version Control System for Machine Learning Projects.

Deep Audio Classifier with Tensorflow

Use Adversarial Networks to generate Images

The NVIDIA Container Toolkit run GPU accelerated Docker containers

Using Flask to deploy your ML Model as a Web Application

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.

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Use Manifold Learning and the LD Analysis to Visualize Image Datasets.

Distribute training across multiple GPUs, multiple machines, or TPUs.

Detect faces in images and compare their feature vector to known entities

Retrieve your Model Data

An introduction to DeepFace Face Recognition with Tensorflow.

Victoria Harbour, Hongkong

LDA is a widely used dimensionality reduction technique built on Fisher’s linear discriminant.

Create a TF Image Classifier that can distinguish between different human emotion based on portrait photos.

Food item segmentation from images of the Tray Food Segmentation dataset

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Shenzhen, China

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Shenzhen, China

Deep Learning Framework with Python for flexibility and C++ for speed.

Shanghai, China

Shanghai, China

Non-linear dimensionality reduction through Isometric Mapping

Keras is built on top of TensorFlow 2 and provides an API designed for human beings.

Building a deep neural network using the MNIST dataset.

Convolutional Neural Networks are ideal for Computer Vision tasks.

Recurrent Neural Networks are widely used to work with sequence data such as time series or natural language.

An example convolutional neural network is the VGG16 Architecture.

Shenzhen, China

LLE is an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data.

Shenzhen, China

MiDaS computes relative inverse depth from a single image.

An open source platform for the machine learning lifecycle.

Experiment to run pyTorch, Jupyter, Hyperopt and MLFlow in Docker

Experiment to run pyTorch, Jupyter, YOLOv8.1 with MLFlow in Docker

Experiment with running pyTorch, Jupyter and MLFlow in Docker

Use ZenML to build a SciKit-Learn SVC Image Classifier Pipeline

Multidimensional Scaling is a family of statistical methods that focus on creating mappings of items based on distance.

Shenzhen, China

Shenzhen, China

Differentiate Objects based on their Contour and Colour with cvZone.

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

To aid visualization of the structure of a dataset, the dimension must be reduced in some way.

Ray is an open-source unified compute framework that makes it easy to scale AI and general Python workloads

Use Ray to deploy your remote services.

Using Ray Serve for ML Model Serving.

Use Ray Actors to maintain a state between invocations.

Remote functions can be run in a separate process on the local machine - spreading out the workload over several cores. Or can be executed on remote machines in your server cluster.

Generate text using a character-based RNN

Predicting Wine Quality with Several Classification Techniques using SciKit Learn.

SHAP is a game theoretic approach to explain the output of any machine learning model.

Use Flask, Docker to Deploy your ML Model to the Web

Use Flask, Docker to Deploy your ML Model to the Web

Use Flask, Docker and React.js to Deploy your ML Model to the Web

Victoria Harbour, Hongkong

Victoria Harbour, Hongkong

Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases

Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases

Cheat Sheet using Psycopg2 to interact with PostgreSQL Databases

Victoria Harbour, Hongkong

Victoria Harbour, Hongkong

Victoria Harbour, Hongkong

Tensor Constants, Variables and Attributes

Tensor Indexing, Expanding and Manipulations

Matrix multiplications, Squeeze, One-hot and Numpy

Computer Vision for Binary Image Classifications

Computer Vision for Multiclass Image Classifications

Working with a non-linear dataset and activation functions

Model Evaluation and Performance Improvement

Model Evaluation and Performance Improvement

Multiclass Classification Problems

Building a Regression Model and Improving it's Performance

Visualizing Models and Evaluating Model Performance

Optimizing model performance

Data pre-processing - normalization and feature-scaling

Working with the medical cost dataset

Using a Pre-trained Model to Extract Features

Fine-tuning Pre-trained Models

Scale a pre-trained model to fit your needs

Use Autoencoders to Reduce Dimensionality and Feature Discovery

Use Autoencoders to Increase Feature Resolution

Generative Adverserial Networks for Image Data Generation

Mong Kok, Hongkong

DeepDream is an experiment that visualizes the patterns learned by a neural network.

Use Tensorflow Serving to Provision your ML Model

Using Representation Learning to Downsample Images

TensorFlow Hub is a repository of trained machine learning models.

The CIFAR-10 is a labeled subset of the 80 million tiny images dataset that can be directly downloaded using Keras.

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Blue print image classifier using Tensorflow and Keras Applications pre-trained models

Build a Denoising Autoencoder in Tensorflow using the mnist Digits Dataset

Once you build a machine learning model, the next step is to serve it with TensorFlow Serving.

Provide your prediction model through the Tensorflow Serving REST API

Tensorflow dashboard that allows you to track the network performance by accuracy and loss statistics.

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.

ViT models apply the Transformer architecture with self-attention to sequences of image patches, without using convolution layers.

Mong Kok, Hongkong

Mong Kok, Hongkong

Mong Kok, Hongkong

Mong Kok, Hongkong

Mong Kok, Hongkong

Mong Kok, Hongkong

Victoria Harbour, Hongkong

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.

Using Tensorflow models in OpenCV to perform semantic segmentations on images using the Mask-RCNN model.

An introduction to Weaviate DB for end-to-end AI applications.

Open Neural Network Exchange (ONNX)

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Shenzhen, China

Getting started with object detection in YOLOv7

Transferring PASCAL VOC labels to the YOLO format

Converting a YOLOv7 PyTorch Model to Tensorflow (Lite)

Use your Custom Dataset to train YOLOv7

I-know-flowers Image Classifier YOLOv8 Model Comparison

Using the YOLOv8 Object Tracker in Combination with EasyOCR

Training an YOLOv8 Object Tracker for Day/Night Cameras

A Modular Libary for YOLO Object Detection and Object Tracking.