Technical interviews can a difficult and stressful part of finding employment. Regardless of whether or not you receive the job offer, you can make the technical interview process a good experience every time. In this session, you will learn some tips for your next technical interview, and also analyze some example interview and coding questions to learn how to think about and answer questions in a way that shows off your abilities.
Git Educated About Git - 20 Essential CommandsJeremy Lindblom
Git is a free, distributed version control system that is fast, easy to learn, and has great features like cheap local branching and convenient staging areas. It has also taken the open source world by storm, especially with the help of online services like GitHub. Learn 20 essential commands that will help you work with your next project, as well as common conventions and workflows.
Amazon Web Services and the AWS SDK for PHP continue to put more power into the hands of PHP developers to build robust and scalable web applications. With version 2 of the SDK, developers now have an even more powerful library for interacting with AWS built on top of existing open source software like the Guzzle HTTP framework and the Symfony 2 Event Dispatcher. In this session you will learn about Amazon Web Services, how to use the AWS SDK for PHP, and how you can easily deploy and scale your applications to the cloud with AWS services, including AWS Elastic Beanstalk.
A presentation about the ideas of recursion and recursive functions.
This is my lecture presentation during A. Paruj Ratanaworabhan’s basic preparatory programming course for freshmen: Introduction to Programming: A Tutorial for New Comers Using Python
Git Educated About Git - 20 Essential CommandsJeremy Lindblom
Git is a free, distributed version control system that is fast, easy to learn, and has great features like cheap local branching and convenient staging areas. It has also taken the open source world by storm, especially with the help of online services like GitHub. Learn 20 essential commands that will help you work with your next project, as well as common conventions and workflows.
Amazon Web Services and the AWS SDK for PHP continue to put more power into the hands of PHP developers to build robust and scalable web applications. With version 2 of the SDK, developers now have an even more powerful library for interacting with AWS built on top of existing open source software like the Guzzle HTTP framework and the Symfony 2 Event Dispatcher. In this session you will learn about Amazon Web Services, how to use the AWS SDK for PHP, and how you can easily deploy and scale your applications to the cloud with AWS services, including AWS Elastic Beanstalk.
A presentation about the ideas of recursion and recursive functions.
This is my lecture presentation during A. Paruj Ratanaworabhan’s basic preparatory programming course for freshmen: Introduction to Programming: A Tutorial for New Comers Using Python
Introductory talk
more technicities in
@inproceedings{schoenauer:inria-00625855,
hal_id = {inria-00625855},
url = {http://hal.inria.fr/inria-00625855},
title = {{A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize}},
author = {Schoenauer, Marc and Teytaud, Fabien and Teytaud, Olivier},
abstract = {{Multi-Modal Optimization (MMO) is ubiquitous in engineer- ing, machine learning and artificial intelligence applications. Many algo- rithms have been proposed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an analysis of restart strategies, and provides a restart strategy for any local search algorithm for which theoretical guarantees are derived. This restart strategy is to decrease some 'step-size', rather than to increase the population size, and it uses quasi-random initialization, that leads to a rigorous proof of improve- ment with respect to random restarts or restarts with constant initial step-size. Furthermore, when this strategy encapsulates a (1+1)-ES with 1/5th adaptation rule, the resulting algorithm outperforms state of the art MMO algorithms while being computationally faster.}},
language = {Anglais},
affiliation = {TAO - INRIA Saclay - Ile de France , Microsoft Research - Inria Joint Centre - MSR - INRIA , Laboratoire de Recherche en Informatique - LRI},
booktitle = {{Artificial Evolution}},
address = {Angers, France},
audience = {internationale },
year = {2011},
month = Oct,
pdf = {http://hal.inria.fr/inria-00625855/PDF/qrrsEA.pdf},
}
It's Not Magic - Explaining classification algorithmsBrian Lange
As organizations increasingly leverage data and machine learning methods, people throughout those organizations need to build a basic "data literacy" in those topics. In this session, data scientist and instructor Brian Lange provides simple, visual, and equation free explanations for a variety of classification algorithms, geared towards helping anyone understand how they work. Now with Python code examples!
Code Fast, die() Early, Throw Structured ExceptionsJohn Anderson
Slides from a short talk given at January 2012 DC.pm. Covers "classic" exceptions in Perl as well as some libraries to make working with exceptions easier.
I am Joanna R. I am an Algorithm Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, the California Institute of Technology, United States. I have been helping students with their exams for the past 9 years. You can hire me to take your exam in Algorithm.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Algorithm Exam.
Weather, opponents, geopolitics: so many uncertainties in such a case ? How to manage power systems in spite of these uncertainties, and how to decide investments.
Talk at Saint-Etienne in 2015; thanks to R. Leriche and to the "games and optimizations" days in Saint-Etienne.
The lengths of pregnancies are normally distributed with mean µ = .docxoreo10
The lengths of pregnancies are normally distributed with mean µ = 268 days and standard deviation σ = 15 days.
25. (a) If one pregnant woman is chosen at random, find the probability that her length of pregnancy is between 260 and 278 days.
(b) Find the number of days above which lie the longest 1.5% of all pregnancies.
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 1/3
Name: Ian Tapia2.5
#1 Points possible: 1. Total attempts: 3
Find for the function.
#2 Points possible: 1. Total attempts: 3
The number (in thousands) of cat flea collars demanded each year when the price of a collar is dollars is
expressed by the function . The collars are currently selling for each and the annual
number of sales is . Find the approximate decrease in sales of the collar if the price of each collar is
raised by .
The approximate decrease in sales is about collars.
#3 Points possible: 1. Total attempts: 3
Find for the function.
#4 Points possible: 1. Total attempts: 3
The monthly revenue (in dollars) of a telephone polling service is related to the number of completed
responses by the function
If the number of completed responses is increasing at the rate of forms per month, find the rate at which
the monthly revenue is changing when .
The monthly revenue is changing by .
y'
2y3 − x4 = − 8
y' =
x p
x
3 + 250p2 = 15, 500 $4
22, 572
$1
y'
3√(y − 1)2 = − 2 + 3x
y' =
R x
R(x) = − 13000 + 15√4x2 + 20x 0 ≤ x ≤ 1000
10
x = 500
$
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 2/3
#5 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#6 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#7 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#8 Points possible: 1. Total attempts: 3
The cost (in dollars) of manufacturing number of highquality computer laser printers is
Currently, the level of production is printers and that level is increasing at the rate of printers per
month. Find the rate at which the cost is increasing each month.
The cost is increasing at about per month.
y' 3x5 + 2y4 − 3 = 26 ( − 1, − 2)
( − 1, − 2) y' =
y' (xy)3 / 2 = 64 (8, 2)
(8, 2) y' =
y' x − 3 + y − 3 = −
7
8
(2, − 1)
(2, − 1) y' =
C x
C(x) = 18x4 / 3 + 12x2 / 3 + 400, 000
729 400
$
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 3/3
#9 Points possible: 1. Total attempts: 3
For the circle ,
find when .
find the slope of the tangent line where .
The slope of the tangent line at is .
find the points at which .
at
If the radius starts increasing at a constant rate of cm/sec, how fast is the area increasing when
cm?
The area is increasing at square cm per second.
#10 Points possible: 1. Total attempts: 3
Find for the function.
#11 Points possibl ...
Machine Learning can often be a daunting subject to tackle much less utilize in a meaningful manner. In this session, attendees will learn how to take their existing data, shape it, and create models that automatically can make principled business decisions directly in their applications. The discussion will include explanations of the data acquisition and shaping process. Additionally, attendees will learn the basics of machine learning - primarily the supervised learning problem.
Introductory talk
more technicities in
@inproceedings{schoenauer:inria-00625855,
hal_id = {inria-00625855},
url = {http://hal.inria.fr/inria-00625855},
title = {{A Rigorous Runtime Analysis for Quasi-Random Restarts and Decreasing Stepsize}},
author = {Schoenauer, Marc and Teytaud, Fabien and Teytaud, Olivier},
abstract = {{Multi-Modal Optimization (MMO) is ubiquitous in engineer- ing, machine learning and artificial intelligence applications. Many algo- rithms have been proposed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an analysis of restart strategies, and provides a restart strategy for any local search algorithm for which theoretical guarantees are derived. This restart strategy is to decrease some 'step-size', rather than to increase the population size, and it uses quasi-random initialization, that leads to a rigorous proof of improve- ment with respect to random restarts or restarts with constant initial step-size. Furthermore, when this strategy encapsulates a (1+1)-ES with 1/5th adaptation rule, the resulting algorithm outperforms state of the art MMO algorithms while being computationally faster.}},
language = {Anglais},
affiliation = {TAO - INRIA Saclay - Ile de France , Microsoft Research - Inria Joint Centre - MSR - INRIA , Laboratoire de Recherche en Informatique - LRI},
booktitle = {{Artificial Evolution}},
address = {Angers, France},
audience = {internationale },
year = {2011},
month = Oct,
pdf = {http://hal.inria.fr/inria-00625855/PDF/qrrsEA.pdf},
}
It's Not Magic - Explaining classification algorithmsBrian Lange
As organizations increasingly leverage data and machine learning methods, people throughout those organizations need to build a basic "data literacy" in those topics. In this session, data scientist and instructor Brian Lange provides simple, visual, and equation free explanations for a variety of classification algorithms, geared towards helping anyone understand how they work. Now with Python code examples!
Code Fast, die() Early, Throw Structured ExceptionsJohn Anderson
Slides from a short talk given at January 2012 DC.pm. Covers "classic" exceptions in Perl as well as some libraries to make working with exceptions easier.
I am Joanna R. I am an Algorithm Exam Expert at programmingexamhelp.com. I hold a Bachelor of Information Technology from, the California Institute of Technology, United States. I have been helping students with their exams for the past 9 years. You can hire me to take your exam in Algorithm.
Visit programmingexamhelp.com or email support@programmingexamhelp.com. You can also call on +1 678 648 4277 for any assistance with the Algorithm Exam.
Weather, opponents, geopolitics: so many uncertainties in such a case ? How to manage power systems in spite of these uncertainties, and how to decide investments.
Talk at Saint-Etienne in 2015; thanks to R. Leriche and to the "games and optimizations" days in Saint-Etienne.
The lengths of pregnancies are normally distributed with mean µ = .docxoreo10
The lengths of pregnancies are normally distributed with mean µ = 268 days and standard deviation σ = 15 days.
25. (a) If one pregnant woman is chosen at random, find the probability that her length of pregnancy is between 260 and 278 days.
(b) Find the number of days above which lie the longest 1.5% of all pregnancies.
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 1/3
Name: Ian Tapia2.5
#1 Points possible: 1. Total attempts: 3
Find for the function.
#2 Points possible: 1. Total attempts: 3
The number (in thousands) of cat flea collars demanded each year when the price of a collar is dollars is
expressed by the function . The collars are currently selling for each and the annual
number of sales is . Find the approximate decrease in sales of the collar if the price of each collar is
raised by .
The approximate decrease in sales is about collars.
#3 Points possible: 1. Total attempts: 3
Find for the function.
#4 Points possible: 1. Total attempts: 3
The monthly revenue (in dollars) of a telephone polling service is related to the number of completed
responses by the function
If the number of completed responses is increasing at the rate of forms per month, find the rate at which
the monthly revenue is changing when .
The monthly revenue is changing by .
y'
2y3 − x4 = − 8
y' =
x p
x
3 + 250p2 = 15, 500 $4
22, 572
$1
y'
3√(y − 1)2 = − 2 + 3x
y' =
R x
R(x) = − 13000 + 15√4x2 + 20x 0 ≤ x ≤ 1000
10
x = 500
$
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 2/3
#5 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#6 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#7 Points possible: 1. Total attempts: 3
Find for at the point .
At ,
#8 Points possible: 1. Total attempts: 3
The cost (in dollars) of manufacturing number of highquality computer laser printers is
Currently, the level of production is printers and that level is increasing at the rate of printers per
month. Find the rate at which the cost is increasing each month.
The cost is increasing at about per month.
y' 3x5 + 2y4 − 3 = 26 ( − 1, − 2)
( − 1, − 2) y' =
y' (xy)3 / 2 = 64 (8, 2)
(8, 2) y' =
y' x − 3 + y − 3 = −
7
8
(2, − 1)
(2, − 1) y' =
C x
C(x) = 18x4 / 3 + 12x2 / 3 + 400, 000
729 400
$
9/16/2016 xyzHomework Assessment
http://www.xyzhomework.com/imathas/assessment/printtest.php 3/3
#9 Points possible: 1. Total attempts: 3
For the circle ,
find when .
find the slope of the tangent line where .
The slope of the tangent line at is .
find the points at which .
at
If the radius starts increasing at a constant rate of cm/sec, how fast is the area increasing when
cm?
The area is increasing at square cm per second.
#10 Points possible: 1. Total attempts: 3
Find for the function.
#11 Points possibl ...
Machine Learning can often be a daunting subject to tackle much less utilize in a meaningful manner. In this session, attendees will learn how to take their existing data, shape it, and create models that automatically can make principled business decisions directly in their applications. The discussion will include explanations of the data acquisition and shaping process. Additionally, attendees will learn the basics of machine learning - primarily the supervised learning problem.
1. Surviving & Thriving in
Technical Interviews
Making your brain do
hard things while
under pressure
2. Surviving & Thriving in
Technical Interviews
Making your brain do
hard things while
under pressure
Why is it
so hard?!
3. What Are Interviewers Looking For?
ü Ability to solve problems
ü Technical skills
ü Soft Skills
ü Team Fit
4. § Understand the problem
§ Think logically
§ Explain Yourself
§ Propose a solution
§ Analyze solution
§ Make improvements
§ Handle changes or constraints
Ability to Solve Problems
5. § Domain-specific Knowledge
§ Data Structures
§ Algorithms
§ Design Patterns
§ Dealing with Large Data Sets
Technical Skills
7. § Used to classify algorithms by their time
complexity – how their processing time
is affected by input size.
§ Basic Classifications:
Big-O Notation & Time Complexity
§ Constant – O(1) or O(c)
§ Logarithmic – O(log
n)
§ Linear – O(n)
§ Linearithmic – O(n
log
n)
§ Quadratic – O(n2)
§ Polynomial – O(nc)
§ Exponential – O(cn)
§ Factorial – O(n!)
8. § The amount of memory cells an
algorithm needs.
§ Often have to evaluate
tradeoffs between
space and time
complexity
§ Doing things "in-place" or not
Space Complexity
9. § Communication
§ Teamwork
§ Leadership
§ Confidence
§ Responsibility
See Amazon Leadership Principles - http://amzn.to/Qb6JB6
Soft Skills
11. I don't mind doing interviews. I don't
mind answering thoughtful
questions. But I'm not thrilled about
answering questions like, 'If you
were being mugged, and you had a
light saber in one pocket and a whip
in the other, which would you use?'
– Harrison Ford
“
”
12. Example #1 – The Raffle
§ Tickets are numbered from 1 to 1,000,000
§ Select 700,000 random winners
§ No duplicates
15. The Raffle – Solution #1
PROS
§ Succinct
§ Uses native functions
§ Pretty fast
§ O(n)
CONS
§ Memory hog!
§ Crashes on higher
numbers
§ Not very random due
to how array_rand()
works
$winners
=
array_rand(array_fill_keys(range(1,
1000000),
true),
700000);
16. The Raffle – Solution #2
$winners
=
array();
for
($i
=
1;
$i
<=
700000;
$i++)
{
$n
=
mt_rand(1,
1000000);
if
(isset($winners[$n]))
{
$i-‐-‐;
}
else
{
$winners[$n]
=
true;
}
}
$winners
=
array_keys($winners);
Note:The "mt" in
mt_rand()
stands for
Mersenne Twister.
17. The Raffle – Solution #2
$winners
=
array();
for
($i
=
1;
$i
<=
700000;
$i++)
{
$n
=
mt_rand(1,
1000000);
if
(isset($winners[$n]))
{
$i-‐-‐;
}
else
{
$winners[$n]
=
true;
}
}
$winners
=
array_keys($winners);
PROS
§ Lower memory than #1
§ Very good randomness
CONS
§ Not really O(n)
§ ~10x slower than #1
§ Extra step to get results
18. The Raffle – Solution #3
$winners
=
range(1,
1000000);
shuffle($winners);
$winners
=
array_slice($winners,
0,
700000);
PROS
§ Fast
§ O(n)
§ Lower memory
than #1
CONS
§ More random than #1,
but not as random as #2
19. CONS
§ More random than #1,
but not as random as #2
PROS
§ Fast
§ O(n)
§ Low memory
The Raffle – Solution #3
$winners
=
range(1,
1000000);
shuffle($winners);
$winners
=
array_slice($winners,
0,
700000);
How To Improve: Use a Fisher-Yates shuffle
algorithm that is seeded with mt_rand().This
would increase the randomness without
negatively affecting performance.
20. The Raffle – Follow Up
What if there were
1,000,000,000 tickets?
21. The Raffle – Follow Up
What if there were
1,000,000,000 tickets?
php
>
$r
=
range(1,
1000000000);
PHP
Fatal
error:
Allowed
memory
size
of
536870912
bytes
exhausted
22. Example #2 – Odd Duck
§ Input: an array of non-negative integers
§ Integers in the array all exist
an even number of times
§ Except for one of them…
Find the "odd duck"
23. Odd Duck – Before You Start
§ It's OK to ask clarifying questions
§ What kind of questions would you ask?
24. Odd Duck – Before You Start
§ It's OK to ask clarifying questions
§ What kind of questions would you ask?
§ Is an empty array valid? No.
§ Is a single-element array valid? Yes.
§ Is the array sorted? No.
§ Can there be more than one instance of the
"odd duck"? Yes.
25. Odd Duck – Before You Start
§ It's OK to ask clarifying questions
§ What kind of questions would you ask?
§ Is an empty array valid? No.
§ Is a single-element array valid? Yes.
§ Is the array sorted? No.
§ Can there be more than one instance of the
"odd duck"? Yes.
…
8
5
42
8
8
9
1
42
1
1
8
9
5
…
27. Odd Duck – Solutions
1. For each number in the array, count the occurrences of
that number and check if it's odd. Bad choice: O(n2)
28. Odd Duck – Solutions
1. For each number in the array, count the occurrences of
that number and check if it's odd. Bad choice: O(n2)
2. Use a 2nd array to count the occurrences of each
number, then look in that 2nd array for the odd one.
29. Odd Duck – Solutions
1. For each number in the array, count the occurrences of
that number and check if it's odd. Bad choice: O(n2)
2. Use a 2nd array to count the occurrences of each
number, then look in that 2nd array for the odd one.
3. Sort the array, and then look for the first occurrence of
a number that exists an odd number of times. (Bonus
points: look in pairs)
30. Odd Duck – Solutions
1. For each number in the array, count the occurrences of
that number and check if it's odd. Bad choice: O(n2)
2. Use a 2nd array to count the occurrences of each
number, then look in that 2nd array for the odd one.
3. Sort the array, and then look for the first occurrence of
a number that exists an odd number of times. (Bonus
points: look in pairs)
4. Use a 2nd array.When you encounter a number, add it
to the 2nd array (as the key).When you encounter it
again, remove/unset it.
31. Odd Duck – Solutions
1. For each number in the array, count the occurrences of
that number and check if it's odd. Bad choice: O(n2)
2. Use a 2nd array to count the occurrences of each
number, then look in that 2nd array for the odd one.
3. Sort the array, and then look for the first occurrence of
a number that exists an odd number of times. (Bonus
points: look in pairs)
4. Use a 2nd array.When you encounter a number, add it
to the 2nd array (as the key).When you encounter it
again, remove/unset it.
5. XOR all of the array elements together. (Oh…)
33. Preparing for the Questions
§ Research your potential employer
§ Be familiar with your own résumé
§ Review the job description
§ Practice technical interview questions
§ Review data structures and algorithms
§ Be prepared for behavioral questions
34. Behavioral Questions
§ Questions related to past experiences
§ "Give me an example of a time when…"
§ "Tell me about something you did that…"
§ "How do you handle a situation where…"
§ Plan some good experiences to share
§ Talk about "I", not "we", and be honest
§ Be prepared to give details
35. Physical Preparations
§ Be well-rested
§ Arrive early
§ Use the restroom before the interview
§ Turn off your phone
§ Assume business casual dress unless you
are told otherwise