Fannie  Zemlak

Fannie Zemlak

1596758400

Cracking the Google Coding Interview with Priyank Goyal

We interviewed Priyank Goyal, who is a Software Engineer at Google, on how to crack the coding interview at Google. Priyank goes in detail about the topics to focus on:

Chapters
3:13 Resume preparation
7:21 Referrals for software engineering jobs
11:44 Resume shortlisting
13:02 Coding interview
15:00 Coding interviews preparation
17:17 Tips and tricks for the interview
20:43 Behavioural interviews

#interview #interview-questions #priyank

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Cracking the Google Coding Interview with Priyank Goyal
Wiley  Mayer

Wiley Mayer

1603904400

How to Prepare for a Coding Interview in 8 Weeks

As of this writing, the market is tough. We’ve been hit hard with a deadly

pandemic that left thousands of people unemployed. It’s layoffs everywhere and the companies are being conservative when it comes to

hiring.

Companies are not willing to hire people with no experience or people who they’ve to train.

Your first job in tech is the toughest, you’re competing

with virtually every new college grad and anyone who completed a boot

camp. I know it can be hard to even land an interview, for someone to

give you a chance to talk and demonstrate you could be valuable

employee.

Now, the chance of you getting an interview totally depends on how your resume compares to the job description. The more relevant it is to the

skills required, the better your chances of getting an interview.

To build your resume, I’d recommend https://thetechresume.com. It’s a nice read to follow the principles when it comes to building a tech resume.

Over the past few months, I’ve been collecting resources like videos,

websites, and taking notes to prepare for coding interviews.

In that process, I made an 8 weeks study guide curated of important data

structure resources to prepare for tech interviews and honestly this

study guide was helpful to me to know what to study every day and in

following a routine for my job search.

Why 8 weeks?

If you’re serious about preparing for a tech interview then 8 weeks is the

minimum to be given to prepare thoroughly for a tech interview. I know

there are few who would cram up pools of content in a week or two. But, I

believe that is not a realistic or sensible approach.

Tech interviews can be intense and most companies expect you to solve problems or go through a data structure topic in detail.

Now, My study guide with resources will eat up the entire blog space. So,

Instead of straight-up dumping down the content all together, I racked

my brains on how to deliver the content in the most effective way

possible to ensure the habit of consistency and dedication stays intact

during the interview preparation process.

In this blog post, I would give you what to cover each week. If you’re

interested to know what resources to refer to when covering each topic then I’d recommend subscribing to the newsletter https://thedailycoding.com in which you’ll receive one email daily about the concept and the resources to practice.

If you believe you can find resources to relevant topics on your own then

here’s how you should plan to cover each topic every week.

#coding-interviews #software-development #job-interview #job-search #coding #latest-tech-stories #coding-interview-tips #coding-job-interview-advice

Fannie  Zemlak

Fannie Zemlak

1596758400

Cracking the Google Coding Interview with Priyank Goyal

We interviewed Priyank Goyal, who is a Software Engineer at Google, on how to crack the coding interview at Google. Priyank goes in detail about the topics to focus on:

Chapters
3:13 Resume preparation
7:21 Referrals for software engineering jobs
11:44 Resume shortlisting
13:02 Coding interview
15:00 Coding interviews preparation
17:17 Tips and tricks for the interview
20:43 Behavioural interviews

#interview #interview-questions #priyank

Jon  Gislason

Jon Gislason

1619247660

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Tyrique  Littel

Tyrique Littel

1604008800

Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

  • J. Robert Oppenheimer

Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast module, and wide adoption of the language itself.

How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

static analysis workflow

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like (, or literals (like integers, strings, e.g., 7Bob, etc.), or reserved keywords of that language (e.g, def in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the tokenize module in its standard library to let you play around with tokens:

Python

1

import io

2

import tokenize

3

4

code = b"color = input('Enter your favourite color: ')"

5

6

for token in tokenize.tokenize(io.BytesIO(code).readline):

7

    print(token)

Python

1

TokenInfo(type=62 (ENCODING),  string='utf-8')

2

TokenInfo(type=1  (NAME),      string='color')

3

TokenInfo(type=54 (OP),        string='=')

4

TokenInfo(type=1  (NAME),      string='input')

5

TokenInfo(type=54 (OP),        string='(')

6

TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")

7

TokenInfo(type=54 (OP),        string=')')

8

TokenInfo(type=4  (NEWLINE),   string='')

9

TokenInfo(type=0  (ENDMARKER), string='')

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

Sheldon  Grant

Sheldon Grant

1620930180

Ace Your Technical Interviews with These GitHub Repositories

Leverage these repositories to ace your next technical and coding interviews

Getting past the technical and coding interview is not always an easy task for most people.

Lucky for you, there are some amazing resources to help you go through easily and grab that position.

In this article, we will go through some of the best GitHub repositories to help you smash the coding interview.

These collections of repositories are essential in highlighting the different arears to focus on and different topics and questions to expect.

Front-end Developer Interview Questions

This repository is everything that entails frontend development.

Covered content includes:

  • General Questions
  • HTML Questions
  • CSS Questions
  • JS Questions
  • Accessibility Questions (external link)
  • Testing Questions
  • Performance Questions
  • Network Questions
  • Coding Questions

#coding-interviews #technical-interview-tips #programming-interviews #interview-preparation #interview