Everything is not same - Perspectives and Clarity matters

20 years of __________________________________________

• 20 years of experience = 20 years of the same project / different projects?
• 20 years of experience = 20 years of same role / multiple roles?
• 20 years of experience = 20 years of services/ product building
• 20 years of experience = 20 years of 9-6 or 9-12 ?
• 20 years of experience = How many endless weekends / production go lives
• 20 years of experience = How many learning migration on skills / domain / data
• Titles vs experience vs Expertise vs Being aware of true self matters

With experience

• Balance both journey and current tasks
• Code to convince someone this is what I meant
• Code to unblock/find next steps
• Code to validate this idea works
• Prototype to share this is feasible

Young folks need time to trust. More than experience connecting with them with all skills/code/experience matters. 

Keep Thinking!!

Zillow Machine Learning Fallout

Good read - Link

Machine learning is no silver bullet if you do not consider domain, data, changing environmental factors. A classic case of missing domain knowledge is flagged in this story.

• Zillow does Real estate - selling, buying, renting, and financing
• Zillow home value estimation models failed.
• Assumption - assumption that housing prices would continue to climb without interruption at a stable rate
• The domain experts warned of issues with the predictions.
• The business went ahead anyway. Finally, it bombed

Lessons

• Domain expert warnings considered as Go / No-go for production, not just model accuracy
• Learn / Incorporate Data Changes to understand changing trends
• Performing A/B Experiments to understand customer behaviors and leverage optimal values based on outcomes
• Better model/feature management / keep improving on features / incorporate external factors based on domain expert perspectives #machinelearning #technology #datascience #domainknowledge

Another good read Zillow, Prophet, Time Series, & Prices

i cannot get over that the zillow data scientist job posting "strongly prefers" you have experience with a python library that is designed to make it as piss easy as possible for little babies to do y_t = f(t) time trend / curve fitting forecasts. pic.twitter.com/YTUcgascCi

— Senior Data Masseuse (@ryxcommar) November 3, 2021

WHY IS INTERMEDIATING HOUSES SO DIFFICULT? EVIDENCE FROM IBUYERS

• Predict that households’ wiliness to pay for liquidity is highest in those markets
• Sophisticated algorithmic pricing

My Perspectives

• I love the housing.com approach to rank an area based on amenities, wellness, connectivity
• Plus a pricing range based on amenities and facilities provided
• Plus growth potential / Availability
• Demand vs Supply

A combination of this would suggest a recommended price that a domain expert could adjust based on other external factors. ML is a guideline, not a blind predictor

Keep Thinking!!!

Leaf Classification

Leaf Classification

Paper #1 - Plant identification using deep neural networks via optimization of transfer learning parameters

Key Notes

• 1.2 million labeled images of 1,000 different categories from the ImageNet = one thousand two hundred per class
• LifeCLEF 2015 - 91,758 labeled images of different plant organs (e.g. flowers, fruits, leaves, and stems), from 1,000 - 91 per class

Parts of Plant

• Branch 
• Entire 
• Flower 
• Fruit 
• Leaf 
• LeafScan 
• Stem 
• Overall

• Increasing the batch size from 20 to 60 improves the overall accuracy
• 80 patches for data augmentation

Paper #2 - Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

Key Notes

• Feature engineering approaches such as Scale-invariant
• feature transform (SIFT), Bag of Word (Bow), Speeded-Up
• Robust Features (SURF), Gabor, Local Binary Pattern (LBP).
• Most generally used features to distinguish leaves of different species
• Hybrid generic-organ convolutional neural network, abbreviated HGO-CNN
• Three different sizes: 256, 384 and 512
• Crop 256 × 256 center pixels
• Multi-Scale Plant Images Generation
• During network training, 224 × 224 pixels are randomly cropped from the rescaled images and fed into the network

Keep Exploring!!!

Face Swapping - Research Reads

Paper #1 - FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

Key Notes

• Early replacement-based works simply replace the pixels of inner face region
• GAN-based works  have illustrated impressive results
• GAN-based network, named Adaptive Embedding Integration Network (AEI-Net)
• Adaptive Embedding Integration Network (AEINet) to generate a high fidelity face swapping result

• DeepFakes, and FSGAN all follow the strategy that first synthesizing the inner face region then blending it into the target face

Paper #2 - Face Swapping: Automatically Replacing Faces in Photographs

Paper #3 - Face Detection, Extraction, and Swapping on Mobile Devices

The Face Swap algorithm consists of five main steps:

• Viola-Jones face detection using Haar-like features [1], Active Shape Model fitting [4], face rotation, skin-tone matching, and smoothing using Laplacian Pyramids [2]. The Viola-Jones face detection uses an OpenCV library [5] to detect faces from a frontal view. 
• Laplacian Pyramid for face 1
• Laplacian Pyramid for face 2
• Laplacian Pyramid after Swapping
• Final Collapsed Pyramid
• Image blending Example
• faceswap-GAN
• FaceSwap
• Faceswap Dev
• Deepfake Faceswap
• DeepFake Tools

More Reads

• DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
• Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward
• Deep Learning for Deepfakes Creation and Detection: A Survey
• DeepFake, CihaNet, SimSwap, FaceSwap-MarekKowal, IPGAN, FaceShifter, FaceSwap-Nirkin, FSGAN, SimSwap
• DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

Keep Exploring!!!