Great Talk - SaaS Startup Opportunity

 

Beautiful Thoughts from KissFlow CEO

• Algorithmic AI is a commodity
• Data is the differentiator
• SaaS - Next Best Opportunity
• 1X revenue - 10X valuation for startups
• Services 1X revenue - 3X value / profits
• 15K SaaS professionals in Chennai
• Zoho = 5X of Freshworks

This slide from Telegram groups gave a different perspective of building products/solutions

Startup Advice

saas metrics

• Net Revenue Retention = (Renewal month to month recurring revenue + Expansion month to month recurring revenue - Churn monthly recurring revenue - Downgrade monthly recurring revenue) / Beginning Monthly recurring revenue
• Gross Revenue Retention = (Renewal Monthly recurring revenue - Churn monthly recurring revenue - Downgrade monthly recurring revenue) / Beginning monthly recurring revenue
• Customer Churn = (Initial Customer Count - Churned Customer count)/ Initial Customer Count
• Customer Acquisition Cost = Sales Spend + Marketing Spend
• Retention Margins = (Top Line Revenue - Cost of Revenue - Customer Service and Success Costs) / Top Line Revenue

Keep Thinking!!!

What is your learning strategy?

Interesting Question - Link
Some answers worth noticing

1. Unless you are working on that tech stack actively, you cannot remember it all
2. Learn fundamentals. Learn only things based on YOUR needs
3. Interviews aren’t totally reflective of the job but one‘s got to pass the interview before getting to the job.
4. Demonstrate competency of core concepts, get the job, and then rise to the occasion as fast as possible
5. Start building the things that you want to build, and you'll learn what you need to along the way.
6. By deliberately selecting your objectives and evaluating possible solutions based on those objectives.
7. Build stuff. Pick out ideas and just build them to build them
8. As a human, you can't be good at everything. You always need to balance between being average in a lot of topics and good in a few topics.
9. Think of a side project you would enjoy that includes a handful of these technologies and start building it. 

My Approach - Every problem these days requires dusting previous memories, read up minimally to recollect paste efforts, spend time connecting the dots and applying to the context 

Happy Learning!!!

Retail - Research Paper Reads

2 Minute Paper Summary.  Key Planogram Papers - Notes
Planogram Compliance Checking Based on Detection of Recurring Patterns

• Planogram Factors - Regulate product placements
• Conventional Planogram Compliance - Done Manually
• Compliance Checking - Detect Product layout, template matching
• Image Processing - Light conditions, viewpoints of product images, image pattern variations due to seasonal promotions

Steps

• Estimated layout vs Estimated product layout
• Spectral graph matching
• Recurring pattern detection

Conventional

• SIFT / SURF
• Cascade object detection

Recurring patterns

• Common visual pattern discovery
• Co-recognition / segmentation of objects
• Object matching with features
• Clustering of images
• Visual words and Objects

Planogram

• Number of rows in shelf
• Number of products in each row
• Type of product vs Shelf Type
• Customer brand positioning
• Competitor band positioning
• Product per customer brand influence
• Products positioned at face height has higher visibility

Metrics

• Customer presence over shelf type
• Customer presence over promotional campaign
• Customer density over shelf type
• Customer density over promotional campaign

Product Characteristics
Product Quantity, Product Size

Paper #2 - Retail Shelf Analytics Through Image Processing and Deep Learning

• Coco, Imagenet do not provide annotation to tackle instore product recognition

Recognize products

• HOG based object detection
• Viola - Jones
• SIFT
• HSV color based

Work

• Mask RCNN
• Region based detectors
• Object Detection
• Instance Segmentation
• Key point detection

For each Image

• Class of product identified
• Bounding Box position
• Number of pixels
• Number of Shelves
• Shelf Type
• Promotional Campaign type
• Cluster type

Metrics calculated on the single Picture
Shelves Number, Cluster, Shelf Type, Promotional Campaign

Implementation

• Object Detection / Mask RCNN
• Query and  find similar images against dataset
• Mask RCNN + VGG for feature extraction + KNN
• brand detector, product detector

Measure

• Predictions vs Ground truth items
• IOU

Experiments

• Per class detection
• Per class segmentation

Paper #3 - Precise Detection in Densely Packed Scenes

Key Notes

• Deep-learning based method for precise object detection
• Estimating the Jaccard index as a detection quality score
• Extensive, annotated data set, SKU-110K
• Soft Intersection over Union (Soft-IoU) network layer
• Represent detections as a Mixture of Gaussians (MoG)

Object detection

• Sliding window-based approaches
• Determine Region Proposals
• Apply Classifiers
• FPN - Feature Pyramid Network

Merge Duplicate Detections

• Non-max suppression
• Agglomerative, affinity propagation clustering

Key Experiments

• Deep IoU detection network
• Soft IOU layer
• EM-Merger unit
• Detection output (x,y,h,w)
• IOU - Intersection / Union (%% of overlap)

EM-approach for selecting detections

E Step assign each box to the nearest box cluster

Box similarity KL distance between corresponding Gaussians

Used for fast clustering

Detection Methods

• Faster-RCNN
• YOLO9000
• RetinaNet
• Infra - Intel(R) Core(TM) i7-5930K CPU@3.50GHz GeForce and a GTX Titan X GPU

Dataset - Link

Presentation - Link

Code - Link. A lot of reusable code is shared in customizing the detections

Poster - Link

Happy Learning!!!

Good Read - Design Patterns

Happy Learning!!!

Good Reads - Data Science

Read Joaquin Quiñonero Candela's answer to In applied Machine Learning what is more important: data, infrastructure, or algorithms? on Quora

Read Joaquin Quiñonero Candela's answer to What do you look for when hiring someone for your team? on Quora

Happy Learning!!!

Basics - Excellent Read for Database Enthusiasts

Basics - Excellent Read for Database Enthusiasts - How RDBMS works

Happy Learning!!!