Interesting Startup - JOTTER.AI

Fashion focused startup on catalog management, creation, realtime smart in-store fashion recommendations

ML areas in the product

• Landmark Detection
• Applying Textures
• GAN based new designs
• Posture based applying designs
• Segmentation to detect different human parts (Upper body, lower body, face, hair etc.)
• 3D reconstruction and applying the selection

I see more startups in line with ideas I have patented and working on :)

Modeling the Human Body in 3D: Data Registration and Human Shape Representation
Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
Whole-body modelling of people from multi-view images to populate virtual worlds
Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer Depth Camera and Synthetic Data
On-line Generation of Customized Human Models based on Camera Measurements
Comparison of Body Capturing and Model Techniques
Convolutional neural network architecture for geometric matching
CNNGeometric PyTorch implementation
CNNGeometric Re-implementation using TensorFlow

Happy Learning!!!

Weekend Learning - Crime detection papers

We know Platir Tech has a crime prediction offering. Some research paper detail out different techniques on crime prediction/detection
Examining Deep Learning Architectures for Crime Classification and Prediction
Key Notes
Inputs for predictive policing

• Who will commit a crime
• Who will be offended
• What type of crime
• In which location
• At what time a new crime will take place

ML Areas

• Predict hotspots
• Predict the possibility for a crime to occur
• Predicting hourly fluctuations in crime rates

Data Patterns

• Spatial and then the temporal patterns
• Temporal and then the spatial patterns
• Temporal and spatial patterns in parallel

Thematic Mapping

• Aggregated to geographic unit areas
• Quick determination of areas with a high incidence of crime
• Visualize geographical extent and duration of crime clusters

ML Features

• 11 crime categories are used as input
• Socioeconomic status and activities
• Data are time-series of data points
• 10 crime types (i.e. “Homicide”, “Robbery”, “Arson”, “Vice”, “Motor Vehicle”, “Narcotics”, “Assault”, “Theft”, “Burglary”,“Other”) 
• Sparsest crime types there is a class imbalance problem
• Data augmentation can come from flipping and rotating the data on their spatial dimension

Paper #2 - Deep Learning for Real-Time Crime Forecasting and Its Ternarization
Key Notes

• Epidemic type aftershock sequence (ETAS) model to crime modeling
• Convolutional neural network (CNN) learn the features for crime forecasting with inputs of historical data, weather, geographical information

ML Features

• Each crime is associated with two times: start and end times
• Crimes in the restricted region
• Temperature, wind speed, and special events, including fog, rain, and thunderstorms
• Trend, period, nearby impact, predict

Britain gave Palantir access to sensitive medical records of Covid-19 patients in £1 deal

Data includes 

• Personal contact details
• Gender
• Race
• Occupation
• Physical and mental health conditions
• Past criminal offenses
• Religious and political affiliation

Happy Learning!!!

What is productivity ? Am I Good Developer ?

The measure we share with Employers / Coworkers 

• 40 hours attendance 
• Responding to emails
• Accounting of tasks
• Attending meetings
• Quickly assemble something working with experiments/assumptions/learning's 

What is productivity at Individual-level?

• Accountability
• Satisfaction
• Preparedness 
• Connecting problems and previous experiences
• Solving newer problems with comfort
• Sharing learnings
• Feeding the learning bus

Ideas take time to implement with the domain, technology, and implementation learnings. Tools are ways to measure/track productivity. In the end, it boils down to an individual level to understand if we really productive?

Learning Cycle needs a lot of perspectives, focus, and consistent efforts.

• Some things I know 'How it works' because I have read about it, observed the working pattern
• Some things I know because 'I have tried and it worked'
• Some things I assume 'It works this way'
• Some things I learn the fundamentals and build my working knowledge on top of it
• The more you learn, the more it pushes you to learn and connect the dots

Am I a Good developer?

Whenever I hear about skill in JD, 

Example Keras - I correlate it to, I have used Deep Learning in this project. Did I master everything in Keras? No. 

How do I know I have learned everything in Keras?

I use Keras to achieve my Deep Learning model, finetune it. The perspective of learning is more towards a good quality MVP, Production code. Getting good quality in terms of performance and scalability needs learning. The focus is not on mastering technology but learning to solve the required Pieces.

What I do about Architecture evaluation?

Way back in 2016 / 2008, Evaluating cloud stack for Retail Analytics, Warranty Redesign, Salesforce Email Integration for XBOX. I was able to pull out a complete end to end architectural components on how the big picture will look like. Did I do hands-on implementation? No, but I was fairly convinced with patterns and use cases and learning How it works or why it works and successful in this context

I am a Prototype developer?

Yes, certain use cases to demonstrate working implementation had to build a working end to end prototype, ML pieces were working pieces, the mobile, web would be mockups built to show end to end flow.

Prototypes to production architecture refinement?

Taking prototypes to production, the Warranty approach to production is the best use case. Able to sell, migrate, implement the new approaches. Initially, Microsoft gave a 1 billion free warranty. We managed to save this hidden cost by ensuring warranty data is recalculated and it's more customer-driven.

Migration analysis

Migrating from old to a new system. Ensuring data is cleaned up. 

Development in all these perspectives is my experience. Somehow my perspective of technology starts from business to technology, I do like data structures, spending time, and learning different implementations. My success or satisfaction is from solving business problems, not from technology learning. Build your vision, future. Keep Going!!!

Learning is Summary of

• Applied Knowledge
• Learned Knowledge
• Experimented Knowledge
• Assumption based Knowledge

I find that number of meetings is actually a good measure of productivity, it's just inversely proportional 😉

— Clare Liguori (@clare_liguori) May 28, 2020

I believe some form of futuristic vision + design thinking is my approach 

What are mathematical thinking and computational thinking and what is the relationship between them? #mathschat #MTBoShttps://t.co/fSWBlH5qAN pic.twitter.com/c3PncZmoBJ

— CambridgeMathematics (@CambridgeMaths) July 13, 2020

Design Thinking : quand coeur, tête et main s'entrecroisent. Un aperçu rapide et quelques principes #DesignThinking #creation #creativité https://t.co/rqPDD7oyWo pic.twitter.com/iiOWKdqro0

— Michèle Drechsler (@mdrechsler) August 1, 2019

Our leading consultant is sharing his experience in Data Science. He suggests applying Design Thinking into Data Science applications. #datascience #designthinking https://t.co/BTHJBVYgGu Y

— Smart Data Institute Limited (@SmartDataInsti1) July 14, 2020

More Read - Link

Keep thinking!!! 

Ten Reasons to launch Orkut2.0

1. The necessity for a better Facebook alternative, New segment of groups outside Facebook need to evolve 
2. Orkut was successful during the 2005+ period. I remember it started as a side project.
3. Google+ started but didn't achieve what Orkut had earlier achieved
4. Android + Orkut 2.0 might need a comeback considering a better Facebook alternative
5. Already many contacts/ frequents contacts are only available in telephone contacts, bringing them into an Orkut 2.0 will challenge both WhatsApp and Facebook
6. Transparent privacy controls and ensuring user confidence Google can attempt to do a better job than Facebook (personal opinion)
7. Segment and build networks kids/elders/peer networks like research gate. We need specialty networks
8. Quora has become a crowded platform and losing its knowledge value. Facebook has become more of an influencer platform. Social media platforms need to be accountable for the facts/opinions shared. If the opinions are shared to disrupt peace then we certainly need to cut down the section that disrupts the peace.
9. Hopefully, there are more user controls, privacy, and data restrictions. The current twitter flagging debate highlights opinions vs verified opinions
10. Time for more innovation! 

Hopefully, we see more alternatives!!!

Learning Notes - Papers - Face Detection / Reidentification Papers

Paper #1 - Facial Keypoints Detection
Key Notes

• Using PCA and LBP 
• Apply different models
• Combine LBP and PCA together

Key Tasks

• Face Alignment
• Face Verification

Key Points locations

• lefteyecenter, righteyecenter,
• lefteyeinnercorner, lef teyeoutercorner,
• righteyeinnercorner, righteyeoutercorner,
• lefteyebrowinnerend, lef teyebrowouterend,
• righteyebrowinnerend, righteyebrowouterend,
• nosetip,
• mouthlef tcorner, mouthrightcorner,
• mouthcentertoplip, mouthcenterbottomlip

LBP (Local Binary Pattern) is an operator used to describe the local texture features of images. It has the  advantages of rotation invariance and gray invariance

Paper #2 - CNN architecture for Key Point Detection presented in Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

Paper #3 - FACE RECOGNITION SYSTEM 
Key Notes

• Face detection
• Face preprocessing 
• Face recognition processes

Facial feature points

• points (eyes, mouth center points, eyes, mouth contour points, organ contour points, etc.)

Siamese Network for Face Comparison

• Siamese network is neural network for measuring of similarity
• It can be used for category identification, classification

Paper #4 - Deep Face Recognition: A Survey

Key Notes

• Face detector is used to localize faces in images
• Facial landmark detector

Face Matching

• Cosine distance or L2 distance
• Nearest neighbor (NN) and threshold comparison 
• With GAN generate Makeup Faces, Similar Fake Faces and Compare

Paper #5 - A Fast and Accurate System for Face Detection, Identification, and Verification
Key Notes

• Deep CNN based detector

Face Detection

• Region proposal Networks
• Sliding window based

Multi-task learning for Facial Analysis

• Simultaneous face detection
• Landmark localization
• Headpose estimation

Single DCNN which can accomplish multiple tasks such as face detection, landmark localization, attribute prediction, age estimation, face recognition

More Reads
KPNet: Towards Minimal Face Detector
Face Recognition Based on the Key Points of High-dimensional Feature and Triplet Loss Automatic landmark annotation and dense correspondence registration for 3D human facial images
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition

Improving Makeup Face Verification by Exploring Part-Based Representations

Keep Thinking!!!

Smart Streetlamps

Link

• Solar Powered
• Inbuild Sensors for Weather, Pollution levels
• Pedestrian Tracking
• Dynamic Digital Signs
• Act as Edge Devices
• Integration with Other Services (Payments / Authentication)

Keep Thinking!!!

Learning Notes - Research papers - Heatmaps

Paper #1 - Revisiting Perspective Information for Efficient Crowd Counting
Key Notes

• Perspective-aware convolutional neural network (PACNN)
• Estimate crowd counts via the detection of each individual pedestrian
• Crowd counting is casted as estimating a continuous density function

Detection-based methods

• Represent the crowd as a group of detected individual pedestrians

Regression-based methods

• Extracting effective features from crowd images
• Utilizing various regression functions to estimate crowd counts
• Edge features, texture features

Perspective information - person scale change along with the perspective geometry

• Blue in the heatmaps indicates small perspective values while yellow indicates large values

High-Level Network Notes

• Generate the GT perspective maps
• Using the K-NN distance to approximate the pedestrian head size
• VGG net backbone
• Three density maps

Paper #2 - Counting Crowded Moving Objects
Key Notes

• Leverage KLT tracker

KLT tracker

• Determine the motion parameters (e.g., affine or pure translation) of local windows W from an image I to a consecutive image J
• KLT runs until no more initial features can be tracked
• Parameter - size of the window

Tracking Challenges

• Inter-object occlusion, self-occlusion, exit from the picture
• Features are agglomeratively clustered into independent objects

Paper #3 - Cross-scene Crowd Counting via Deep Convolutional Neural Networks
Key Notes

• Develop effective features to describe crowd
• Different scenes have different perspective distortions, crowd distributions and lighting conditions.
• CNN Model to detect crowds
• Find Clusters of People
• Apply models to count people in each cluster
• Patches and Density Detection

Paper #4 - Comparison of Tracking Techniques on 360-Degree Videos
Evaluation Criteria

• Viewpoint
• Occlusion
• Deformation
• Lighting
• Scale
• Shakiness

Summary of Trackers

Paper #5 - Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
Key Notes

• Counting 
• Clustering of points
• Creation of Density Map

Keep Thinking!!!

Interesting Startup - Smart Farming - root-ai

This could be a good addition to monitor, manage, harvest fruits.
AI Components

• Fruit Detection
• Fruit Ripening Estimation
• Robotics and Navigation Components
• Robotics Movements of harvesting without harming the plant

Smart Farming

• Planting Seeds
• Smart monitoring, watering drip irrigation based on humidity, moisture, weather 

There is another similar startup which I have been following for sometime https://farm.bot/
More Reads

• Fruit recognition from images using deep learning
• Detection of Overall Fruit Maturity of Local Fruits using Convolutional Neural Networks Through Image Processing
• Spoilage Detection in Raspberry Fruit Based on Spectral Imaging Using Convolutional Neural Networks
• Banana Ripening State Classifier

Agriculture meets tech and the result is awesome#agritech #futureofwork #technology #Robotics #Robot #innovation | @enricomolinari @Nicochan33 @ingliguori @evankirstel @Droit_IA @sebbourguignon @jblefevre60 @jamesvgingerich @labordeolivier @PawlowskiMariopic.twitter.com/7DtbpRkHbj

— Hana (@Hana_ElSayyed) May 30, 2020

This agricultural spraying #drone works with an #AI Engine and #3D planning@IntEngineering #Drones #UAV #AgriTech #Innovation #Automation@alvinfoo @chboursin @mvollmer1 @NevilleGaunt @DrJDrooghaag @kalydeoo @labordeolivier @Fabriziobustama @AkwyZ @robvank @DigitalColmer pic.twitter.com/Jf7DIxBtrS

— Franco Ronconi 🇮🇹 (@FrRonconi) May 28, 2020

At FarmWise, we harness the power of #AI to find solutions to the worldwide #food supply challenge, and help growers thrive in this new #farming era. Join our team: https://t.co/OewFdD7C37#agtech #agritech #agriculture #artificialintelligence #data #robotics #IoT #agrobotics pic.twitter.com/B2HT1LPNj8

Look: no hands and no driver! This autonomous tractor by @bfrobotics demonstrates mowing a field at the @forbes #ForbesAgTech yesterday. #bearflagrobotics #AgTech pic.twitter.com/8QreN69bU8

— AgriNovus Indiana (@AgriNovusIN) June 28, 2018

Happy Thinking!!!

Day #2 - Data Management Course Notes

Link
Module #4 - Master Data Management
Key Notes

• Master data, agreed and shared across Enterprise (Customer, Employee)
• Reference data is a subset of master data (Country Code, Industry Classification)
• Data Representation differences - MM/DD/YYYY, DD/MM/YY, Identify potential matches, apply business rules and merge records
• MDM creates master records with consistent data representation
• Have a lookup to reference table with possible different representations, Update sources with valid state names

Module #5 - Data Integration

• Scenarios  - ETL, ELT, Batch, and Real-time Integration
• Data Source -> Integration -> Target Systems
• Source -> staging -> DW
• Calculations, Aggregations during ETL
• Batch Processing, Real-time integration

Module #6 - Analytics

• Reporting and Analytics
• Monitor, Understand and improve business
• Cross-Selling, Upselling
• Marketing Insights
• Promotional Campaigns
• Dashboards, Visualization, Alerts, Conditional Reports
• Data mining, Stats, Text Analysis

Module #7 - Data Architecture
Models, Rules, Policies that govern definition, storage

Module #8 - Privacy

• Protect Sensitive data
• Unauthorized Access
• SSN, DOB, CreditCard, SalesPlan
• Governance - Rules, Privacy - Protect Rules
• Email Protection, Antivirus, Firewalls, WIFI encryption, Cloud data storage systems, Secured Network Access   

Happy Learning!!!

Learning Notes - Marketing your Products

This fantastic link Marketing-for-Engineers  has a list of resources, curated talks, pointers from a Marketing perspective.

Some key Talks and Notes
Talk #1 - How To Create UX Personas
Persona

• Type of customers
• Industry, device, time, goals of customers
• Identify customers, patterns
• Connect with them
• Observe, Capture the findings and add more data points
• Insights into customers
• Track changes to personas over time

Talk #2 - What is an Empathy Map?

• Understand user thinking, feeling, saying and doing
• Conducting moderated sessions to understand users
• Ask a lot of open-ended questions
• Understand and prioritize user needs
• Capture explicit opinions, implicit signals/response
• Created based on aggregated users across gender/age groups etc..

Talk #3 - Customer Development and Lean Startups
Venture performance = product development + customer development + team building + luck
Venture performance = learning rate | product development | customer development | team | luck
Analyze market, product, customers
Methodology to go from unknown to known. Customers, products, business models

Step by Step process

• Define
• Layout Steps
• Make Observations
• Analyze data
• Conclusions / Further Experiments

Ways to Reach out

• Reach out to existing network
• Learn from competitors
• Local meetup
• Social media
• Local incubators

Zero Cost Marketing

• Blogs
• Free Resources / Tools
• Collaborating with like-minded folks

Startup - Search for a business model, Transition to next phase scale-up, Execute the model

Happy Learning!!!

Learning Notes - Edge Devices - Bench marking

Paper #1 - A Survey on Edge Benchmarking

Edge benchmarking parameters

• I/O throughput
• Data staleness
• End-to-end communication or computation latency

Devices

• Intel Movidius Myriad X VPU
• NVIDIA 128-core Maxwell and 256-core Pascal architecture-based GPU
• Google Edge TPU

Paper #2 - MLPERF TRAINING BENCHMARK
Tasks Considered

• Image classification
• Object detection (lightweight)
• Instance segmentation and object detection (heavyweight)
• Recommendation
• Reinforcement learning 

Modifiable Hyperparameters

• Batch size, Learning-rate schedule parameters
• Optimizer: Adam or Lazy Adam, Learning rate
• Maximum samples per training patch

MLPerf Training v0.6 Results
Paper #3 - Early Experience in Benchmarking Edge AI Processors with Object Detection Workloads

Paper #4 - pCAMP: Performance Comparison of Machine Learning Packages on the Edges

More Reads

• A Hybrid Approach to Privacy-Preserving Federated Learning
• EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing
• The Big Benchmarking Roundup
• How to set up a Postgres database on a Raspberry Pi
• A Step by Step guide to installing PyTorch in Raspberry Pi
• Federated Learning On Raspberry Pi
• FEDERATED LEARNING OF A RECURRENT NEURAL NETWORK ON RASPBERRY PIS
• How to set up a Raspberry Pi with Ubuntu from almost any device, completely headless, with just WiFi
• Object Detection on NVIDIA Jetson TX2
• Benchmarking Edge Computing

Keep Thinking!!!

Day #1 - Data Management Course Notes

Course Link

Key Notes
Module #1 - Introduction Data Management 

• Development and execution of Architecture, policies, procedures to manage data

Key Capabilities (People, Process, Technology Aspects for each Capability)

• Metadata management
• Data Quality
• MDM
• Data Governance
• Data Integration
• Analytics
• Data Privacy
• Data Architecture

Data Element - Representation of data. Attributes, permissible values, Identification defined.
Critical Data Element - Key elements capturing business process. Examples - Business Facts, Support Business Process, Data appears in Key Reports, Unique Identifiers - CustomerId, SupplierId
Metadata management

• Data structures from different models
• Information about Attributes, models, columns, glossary

Data - Definition, Business Rules, Ownership, Logical Data Model, Physical data - Schema
Data Sources - OLTP, OLAP, Integration - Data Movement
Business Metadata - From Business Perspective, ownership. Customer Name - Client Name, Legal Name, Trade Name. Rules to validate those names
Roles - Business Owner, Data owner, technical owner
Technical Metadata - Entities, Attributes, Mutual Relationships, Associations
Data Lineage - Traceable path from data sources, data marts, data warehouses
Identify Data Elements, Collect Business, Technical Metadata, Enforce Data standard
Tools - ETL tools, Modelling tools, BI tools, Domains, Definitions, values, Hierarchies. With all structured, unstructured data this would be done at data lake.

Module #2 - Data Governance

• Availability, Usability, Integrity, and Security of Data
• Establish a process for standards
• Same policies across the organization
• Leadership, Data Standards, Ownership, Monitoring, Change Control, Executive Support
• Hierarchy - Business Sponsor - Council - Data owners

Module #3 - Data Quality Management

• Approach, policy, procedure for accuracy, timeliness, completeness, and consistency of data in system and data flows
• Data Questions like accuracy, validity, on-time arrival, completeness, uniqueness, consistency

Technical tasks

• Data Profiling, Set Rules, RCA for identified issues, Resolutions, Set a threshold and identify accuracy percentage detected

Happy Learning!!!

Learning Notes - Fashion Recommendation Papers

Paper - REDEFINING THE OFFLINE RETAIL EXPERIENCE: DESIGNING PRODUCT RECOMMENDATION SYSTEMS FOR FASHION STORES
Keynotes

• Leverage sensor technology, novel customer services
• Smart fitting rooms that offer garment recommendations

Attributes

• Sensor capabilities of smart fitting rooms
• Algorithms for Brick & Mortar recommendation systems
• Contextual attributes

Algorithms

• Content-based methods were similarities between item features are taken into account
• Collaborative-filtering approaches where product suggestions are based on the previous behavior of users with similar preferences

User cold start problem

• Using social media profiles to deduce customers’  preferences
• Adoption of association rule mining algorithms for product recommendations

Absence of explicit product ratings

• Propose combining them with clustering approaches
• Make association rules less generic and more customer-group-specific

Contextual Information in Brick and Mortar Stores

• Special attention must be paid to the selection of contextual attributes
• Location and time
• Trends and occasions
• User locations
• Customer interactions with products
• Store locations
• season, occasion, weather
• Categories activity (e.g., trying on garments)

Analysis of Data

• Patterns of buying across seasons

How to handle a cold start?
1. When customer attributes / product recommendation not available, use the popular item (Frequently bought from transactions)
2. Recommendations based on products customers bring into fitting rooms, Recommendations based on Apriori
3. Target individual customers if they identify themselves

Smart Mirror: Intelligent Makeup Recommendation and Synthesis

Key Notes - Model to generate/identify facial features, facial attributes, and makeup attributes recommendations
ML work

• Facial feature extraction - facial landmark point extraction
• Regions of Lips, eye, hair and face points extracted
• Makeup recommendation and synthesis. Recommendations based on eye shadow, skin color, and lip color
• Apply recommendations for eye, lipstick, hair color
• Apply makeup on the face

Keep Thinking!!!

Learning Notes - Convex Optimization in Python with CVXPY

Key Notes
Convex Optimization problem

• Decision variable to solve
• Objective Function
• Inequality Functions
• Linear equality constraints
• All functions are convex
• Convexity - Positive curvature, curve upwards like parabola

Useful in below fields

• Applies in various fields
• All ML algos based on Convex Optimization
• SpaceX landing Convex Optimization

How to Solve ?

• Using Solvers for particular form of problem
• Linear program, quadratic problem, second order con program

Convexity Verification

• Using Disciplined convex programming
• Determine curvature of every node in expression tree

cvxpy

• Developed in python
• Modelling framework
• Parameter assignment
• Simulation visualization
• Energy Management (Load Demand, Battery Charge / Discharge / Price margin / Load)

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!!!

Weekend Learning - Convex Optimization - Stephen Boyd, Professor, Stanford University

Key Notes
Mathematical Optimization

• Choices of a vector/numbers
• Constraint - Legal / Technical / Physics
• Judged by objectives
• Examine on profit/utility

Purpose

• Make good actions
• Reduce risk/ cost is objective / action
• Constraint come from the manufacturing process

Variables

• Vector x could be trades, schedule 
• Resource allocation 
• Optimize signals

AI / Stats / ML

• X - parameters to model
• Constraints (impose requirements)
• Optimization used for worst-case analysis

Optimization-based models

• Aggregate small number of agents
• Simplistic assumptions and formulate the problem
• Predictive ability of models

Convex Optimization

• Minimize objective
• Constraint to hold
• Linear constraints
• Constraints and linear functions will curve up

Why?

• Methods available to solve them

Different application areas

• Spacex landing is effort of optimization
• Optimal trajectory to landing path
• 10 times a second
• Networking / Circuit design

How to use ?

• Formulate as convex problem

Examples

Example #1 - Radiation treatment planning

• Things decided are actions
• linear y = Ax
• options - beam diverges / tissues / hits bone scatters
• Overcharge / Undercharge

Example #2 - Image in painting

• Guess the lost parts
• Minimize function / Convex problem
• Remove 5% of pixels

SVM

• Predict boolean outcome
• spam/ fraud 
• Old school - gradient method
• Convex Optimization - Differentiability irrelevant

Lasso

• Methods for sparse model construction
• With 1/5th measurements analyze

Solving

• Define in High Level language
• Solved by solver
• Helps in rapid prototyping

Large Scale Distributed Optimization

• Grid Updates
• Image / Video processing

Read Ferenc Huszár's answer to Why is Convex Optimization such a big deal in Machine Learning? on Quora Happy Learning!!!