Day #269 - Age and Gender Estimation from Face

Happy Learning!!!

Completing one Training session of Deep Learning

Today I completed my 10th class of Deep Learning. It was fantastic learning, discussion with the participants. I am very thankful for the students for healthy discussions and sessions.

Learning never ends. The consolidated feedback looks like below

• Feedbacks - 25
• Excellent  -16
• Good - 4
• Average -2
• Bad -3

Keep Learning. Keep Going!!!!

Day #268 - Techniques for unique vehicle identification

• Finding with cars passing the mid-point (Approach #1 – Experimented with SQL)
• Take the bounding boxes between two frames and check the similarity between the matching regions. Between the two frames and the matching regions how much does the content match. This can be used to remove duplicate cars
• Generate a feature vector for the Area of Intersection and validate with the frame if it is similar. 

Advanced methods

• KNN approach – Experiment KNN approach with the data points to classify a new or existing object
• LSTM based tracking – Model the frames into sequences to classify if it is a new object or existing object. It is possible, Needs a bit more analysis
• Siamese network for Similarity of Objects 

Happy Learning!!!

Day #267 - Get Historical Weather Data

Fetch Historical weather data information to correlate with findings


Happy Learning!!!

Day #266 - Face Recognition Examples - Package - face_recognition

Use Cases
1. Extract Face Region
2. Search for Face Match



Deep face recognition with Keras, Dlib and OpenCV

Key Research Papers - Face Recognition

London bar uses #FacialRecognition
by @tictoc#Tech #Technology #IT

Cc: @evankirstel pic.twitter.com/z2I1mClzU9

— Ronald van Loon (@Ronald_vanLoon) July 14, 2020

Happy Learning!!!

Thinking Efficiency

Metrics to measure and improve your thinking

Number of creative ideas
Number of newer perspectives 
Number of simple solutions
Number of similar ideas analyzed
Communicate and get feedback on ideas
The complete end to end clarity of implementation 
The visualization of implementation in the end-user perspective
Explore and build incrementally

Happy Learning and Teaching!!!

Day #265 - Python Arrays & Version Check of TF,OpenCV

Happy Learning!!!

Day #264 - LSTM One Pager

Happy Learning!!!

Probability Notes and Concepts

• Prior distribution that incorporates your subjective beliefs about a parameter  
• Posterior is the result with data
• Likelihood - Based on the posterior the how ‘likely’ is the data is going to occur
• Bayesian Linear Regression - The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution. Determine the posterior distribution for the model parameters
• Latent variables as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) 
• A Bernoulli random variable has two possible outcomes: 0 or 1. A binomial distribution is the sum of independent and identically distributed Bernoulli random variables.
• Poisson distribution - events occurring in a fixed interval of time or space if these events occur with a known constant rate and independently of the time since the last event
• Negative binomial distribution describes the number of successes k until observing r failures

Happy Learning!!!

Day #263 - Reviewing Interesting Deep Learning Projects

For days I haven't been able to catch up on my technical debts. Too many things running in mind. I feel my time is getting reduced and the things to accomplish are more. Hoping to priotitize and be productive coming days

A ton of projects listed in cs231 reports. This is a great way to understand applications. Reviewing a few of them every day

Project #1 - ConvNets for Intelligent Baby Monitoring
Poster - Link 

This was my dream product idea. A year back we were discussing on the same. They have done a fantastic job in analyzing the idea end to end.

Summary 
Verified in different architecture - ResNet18[1], AlexNet[10]and SqueezeNet[6]. Different camera position was evaluated

Challenges

• Highly unbalanced dataset
• Different camera viewpoints, different rooms/cribs, different babies, different lighting conditions and different toys inside the crib

Motion detection techniques

• Image subtraction
• Optical flow
• Temporal difference method

Manually classify into 5 classes

• Caretaker
• Empty Crib
• Sitting Up
• Lying Down 
• Standing Up

Great Idea and well-implemented!!!

Happy Learning!!!