Artificial intelligence and machine learning are among the most significant technological developments in recent history. Artificial intelligence is moving forward, and whether we like it or not, machine learning will play an essential role in our technological future. The largest and best companies in the world already know this, and they are investing heavily in AI.Artificial … [Read more...] about Benefits which MNC’s are getting from AI/ML
Machine Learning
Pearson’s Correlation
Pearson’s Correlation is the Feature Selection Method.It shows direction and strength between dependant and independent variables.This method best suited when there is a linear relation between dependant and Independent.Its value ranges from -1 to 1.-1 means there is strong -ve relation between dependant and independent.0 means there is no relation between dependant and … [Read more...] about Pearson’s Correlation
Part 2: Starting off with Image Segmentation using Mask R-CNN!
Credits: Matterport, IncSee that picture above? That is what this part is all about.We will use Matterport’s pre-trained COCO dataset (mask_rcnn_balloon.h5) & and annotated images ( balloon_dataset.zip) to test how the model works and what to expect.To test the model, we will be using a Jupyter Notebook!Head to your Anaconda Navigator and open a Terminal window in the SAME … [Read more...] about Part 2: Starting off with Image Segmentation using Mask R-CNN!
Part 1: Starting off with Image Segmentation using Mask R-CNN!
No. Image Detection and Segmentation does not require a PhD.Yes. It truly is amazing that we can access and use numerous libraries for free without understanding the complexities behind them.This series mainly focuses on getting started with Image Segmentation using Mask Regional Convolutional Neural Network (MRCNN) using Python!Before anything, I would like to credit … [Read more...] about Part 1: Starting off with Image Segmentation using Mask R-CNN!
Enhancing Kubeflow with MLFlow
MLFlow and KubeflowEveryone who have understanding in machine learning understands that machine learning model development is different from traditional software engineering problems. Many tools have to the market trying to solve this problem. Before we start, check out my previous article on why we need MLOps, link to the article: … [Read more...] about Enhancing Kubeflow with MLFlow
機器學習基石系列(6) — 線性回歸和機器學習
從本篇開始會陸續介紹幾個演算法，或許大家都耳熟能詳，甚至可以說是統計相關的基礎方法。不過，這次要從機器學習的角度來看它們。首先登場的是線性回歸。根據資料來決定是否發給客戶信用卡，這是一個二元分類問題。如果把問題改成：該給多少信用額度，這就變成一個連續數字的問題了。根據資料來決定多少額度，以機器學習而言，輸入還是資料，輸出從二元分類變成連續性的實數空間。如何設計這個演算式子呢?我們用加權處理：把每一筆資料乘上權重，並加上常數項d_0，形成然後追求y和實際y的接近程度。到這一步，其實做的事情和前面PLA很像，差別只在最後沒有取符號，不是只看正負，而是要看確實的數字。因此得到的結果直接輸出就可以了。這叫做線性(回歸)假設。若x為一維資料，則其對應的y可以化成直角坐標，線性假設就是座標上的一條線，而每個資料點實際的y和線性假設的落差(距離)就是垂線。若x為二維 … [Read more...] about 機器學習基石系列(6) — 線性回歸和機器學習
Better Urban Walkability
Step 1, Generate DataWe used Grasshopper 3D, a well-known architectural design tool, to generate our training data set. The advantage of Grasshopper 3D for generating the data, is that it uses a visual interface that makes it easy for non-coder designers to get involved. On an average the professionals would spend close to 60–80% of the time cleaning and wrangling the data. … [Read more...] about Better Urban Walkability
Semi-Supervised Learning
In order to understand semi-supervised learning, we should understand supervised and unsupervised learning first.A supervised learning algorithm learns from labeled training data, helps you to predict outcomes for unforeseen data.An unsupervised learning algorithm learns patterns in unlabeled data.Semi-supervised machine learning is a combination of supervised and unsupervised … [Read more...] about Semi-Supervised Learning
Python影像辨識筆記(二十九)：Zero-Shot Classification — Latent Embedding Feedback and Discriminative…
介紹這篇論文是目前Zero-Shot Classification的state-of-the-art。可以參考：https://paperswithcode.com/task/zero-shot-learning、https://paperswithcode.com/sota/zero-shot-learning-on-cub-200-2011在各個不同Dataset上的實驗結果使用方式# 需特別注意，這份程式使用PyTorch 0.3.1版本，且repo裡面提供的安裝方式在我的環境當中會有錯誤產生，因此使用以下的方式來進行安裝及訓練conda update conda# 安裝0.3.1的PyTorch# 參考https://www.ptorch.com/news/145.htmlpip install … [Read more...] about Python影像辨識筆記(二十九)：Zero-Shot Classification — Latent Embedding Feedback and Discriminative…
Data Representation Techniques to Supercharge your ML model — Part I
How to do feature engineering beyond scaling and one-hot encodingPhoto by Annie Spratt on UnsplashBeing a data scientist is like being a craftsman. You are equipped with a set of tools and required to create something beautiful yet functional out of simple material. Some of your work might be done by automatic machinery, but you know that your true power is creativity and … [Read more...] about Data Representation Techniques to Supercharge your ML model — Part I