IRAWEN

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• Small-leaved Barringtonia
• Black-rumped flame back
• Powering Sky thru Reflection
• Mount Panchachuli group of peaks, from Nandadebi park Munsiyari, kumaon himalaya, uttarakhand
• BIRD IS LOOKING FOR PREY
• Yume Samdong (Zero point), Lachung, North Sikkim
• Machine Learning Projects | Machine Learning Project Ideas For Beginners
• Simple Linear Regression - Coding Least Square Error | NerdML
• Multiple Linear Regression & Polynomial Regression | NerdML
• Simple Linear Regression - Gradient Descent | NerdML
• Bayesian Linear Regression | NerdML
• Machine Learning - Use of Mathematics | NerdML
• Machine Learning Introduction | NerdML
• Bias-Variance || Overfitting & Underfitting | NerdML
• Simple Linear Regression - Mathematical Explanation | NerdML
• Chapman Drive South Africa
• Plovers on the Beach
• Introduction to virtual Buton in Augmented Reality
• Data Mining in Agriculture (Springer Optimization and Its Applications Book 34) Kindle Edition by Antonio Mucherino (Author), Petraq Papajorgji (Author), Panos M. Pardalos (Author) PDF
• Striking Power

Small-leaved Barringtonia Posted: 12 Jul 2020 10:19 PM PDT PC: T H Tsao

Black-rumped flame back Posted: 12 Jul 2020 10:05 PM PDT PC: A Dhar

Powering Sky thru Reflection Posted: 12 Jul 2020 10:05 PM PDT

Mount Panchachuli group of peaks, from Nandadebi park Munsiyari, kumaon himalaya, uttarakhand Posted: 12 Jul 2020 10:05 PM PDT  PC: Debashish Dey

BIRD IS LOOKING FOR PREY Posted: 12 Jul 2020 10:05 PM PDT PC:  Somsubhra Roy

Yume Samdong (Zero point), Lachung, North Sikkim Posted: 12 Jul 2020 10:05 PM PDT PC: Utpal Sarker

Machine Learning Projects | Machine Learning Project Ideas For Beginners Posted: 12 Jul 2020 09:51 PM PDT Machine Learning Steps : Collecting Data Data Wrangling Analyse Data Train Algorithm  Test Algorithm  Deployment Type of Learning Supervised Learning Reinforcement Learning Unsupervised Learning 1. Supervised Learning : Regression              *)  Linear              *)  Polynomial Decision Tree Random Tree Classification             *) Logistic Regression            *) Naive Bayes            *) SVM 2. Reinforcement Learning Q - Learning SARSA DQN DDPG 3. Unsupervised Learning Clustering                *) SVD               *) PCA               *) K-Means Association Analysis              *) Apriori             *) FP-Growth

Simple Linear Regression - Coding Least Square Error | NerdML Posted: 12 Jul 2020 09:58 PM PDT Simple Linear Regression - This video will help you to understand that Simple linear regression is a statistical approach that allows us to study relationships between two continuous (quantitative) variables: dependent (Y variable) & independent (X variable). What we have studied in last session we have coded in this session with & without using Scikit-learn library.

Multiple Linear Regression & Polynomial Regression | NerdML Posted: 12 Jul 2020 09:59 PM PDT This video will help you to understand Multiple Linear Regression, how to generate a straight line when we have more than one independent variables. We'll see the use of polynomial regression, why it is known as linear regression & we'll derive the equation for polynomial regression. Below topics are explained in this video: 1. What is Multiple Linear Regression? ( 00:57 ) 2. What is Polynomial Regression? ( 06:03 )

Simple Linear Regression - Gradient Descent | NerdML Posted: 12 Jul 2020 09:59 PM PDT Simple Linear Regression using Gradient Descent - This video will help you to understand that Simple linear regression can be solved by Gradient Descent as well . I have tried to cover all aspects including inner functionality of this algorithm using gradient descent, mathematics derivations used behind this & coded a snippet. Below topics are explained in this video: 1. Introduction? ( 00:06 ) 2. Cost Function ( 00:33 ) 3. Gradient Descent mathematical explanation ( 01:40 ) 4. Finding the derivative of a Cost function using gradient descent (02:43) 5. Mathematical calculation on the actual dataset (06:38) 6. Coded snippet for finding best fit line using gradient descent (07:50)

Bayesian Linear Regression | NerdML Posted: 12 Jul 2020 09:59 PM PDT Bayesian Linear Regression - This video will help you to understand the drawbacks of solving Linear Regression problems using Least Square Error or Gradient Descent over Statistical approach of Bayesian Learning. Derived mathematical functions of Bayesian learning. Below topics are explained in this video: 1. Frequentist vs Bayesian approach? ( 00:15 ) 2. Case Study on Frequentist approach ( 00:55 ) 3. Bayesian Approach over Frequentist Method for solving Linear Regression problems ( 05:07 ) 4. Why we need to use Bayesian Learning for solving Linear Regression problem (05:49) 5. Linear Regression solution using Bayesian Learning (08:14)

Machine Learning - Use of Mathematics | NerdML Posted: 12 Jul 2020 09:59 PM PDT Mathematics used behind Machine Learning - This video will help you to understand what is the use of Mathematics in Machine Learning. Why I started this series of tutorial? All will be answered in this video. Below topics are explained in this video: 1. Why we use Mathematics in Machine Learning? ( 00:25 ) 2. Prerequisites of Mathematics ( 2. Prerequisites of Mathematics ( 00:55 ) 3. Explanation of use of Maths using Linear Regression example? ( 3. Explanation of use of Maths using Linear Regression example? ( 03:22 ) 6. Recap for next video ( 6. Recap for next video (06:14)

Machine Learning Introduction | NerdML Posted: 12 Jul 2020 10:00 PM PDT This Machine Learning tutorial will help you to understand what is Machine Learning, what are the types of Machine Learning - supervised & unsupervised, how Machine Learning works with simple examples, and will also explain how Machine Learning is being used in various industries. I have tried to cover all aspects including mathematics derivations used behind algorithms, the functionality of algorithms & code these algorithms in python. Below topics are explained in this video: 1. What is Machine Learning? ( 00:34 ) 2. Types of Machine Learning ( 01:42 ) 3. Explanation of Supervised Learning? ( 01:47 ) 4. Explanation of Unsupervised Learning? ( 02:42 ) 5. Quiz (03:19) 6. Recap for next video (03:24)

Bias-Variance || Overfitting & Underfitting | NerdML Posted: 12 Jul 2020 10:00 PM PDT This video will help you to understand What is Bias & how does it work? What is variance & how to mathematically calculate variance on data-points? What is Overfitting & Underfitting? How to avoid Overfitting & Underfitting by trading-off bias-variance. Below topics are explained in this video: 1. What is Bias & how does it work? ( 00:23 ) 2. What is variance & how to mathematically calculate variance on data-points? ( 02:17 ) 3. What is Overfitting & Underfitting? ( 04:55 ) 4. How to avoid Overfitting & Underfitting by trading-off bias-variance? (10:58)

Simple Linear Regression - Mathematical Explanation | NerdML Posted: 12 Jul 2020 10:00 PM PDT Simple Linear Regression using Least Square Error - This video will help you to understand that Simple linear regression is a statistical approach that allows us to study relationships between two continuous (quantitative) variables: dependent (Y variable) & independent (X variable). How this works with simple examples, and will also explain how Simple Linear Regression is being used in various industries. I have tried to cover all aspects including inner functionality of this algorithm, mathematics derivations used behind Simple Linear Regression. Below topics are explained in this video: 1. What is Simple Linear Regression? ( 00:08 ) 2. Why Least Square Error ( 02:51 ) 3. Mathematical explanation of Simple Linear Regression? ( 03:35 ) 4. Recap for next video (06:13)

Chapman Drive South Africa Posted: 12 Jul 2020 10:05 PM PDT PC: Alan Kawadler

Plovers on the Beach Posted: 12 Jul 2020 10:05 PM PDT Sunset at Ventura Harbor Beach PC: Ofer Dub

Introduction to virtual Buton in Augmented Reality Posted: 12 Jul 2020 10:00 PM PDT Virtual Button is a clickable on the Image Target using which we can interact with the Image Target. Six (6) important factors to consider 1. Size  : Virtual Button should ideally have an rectangular shape. 2. Placement of Virtual Button : Area's on image Target with maximum or many yellow dot's are called featured area's. 3. Virtual Button should not be placed on Borders of Image Target. 4.  Multiple virtual button's should be placed in ROW arrangement 5. Sensitivity of Virtual Button  : LOW MEDIUM and HIGH 6. Texture area on which virtual button is placed

Striking Power Posted: 12 Jul 2020 10:05 PM PDT

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