An approximate introduction to how zk-SNARKs are possible (part 1)
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has 2 very powerful applications: Perhaps the most powerful cryptographic technology to come out of the last decade is general-purpose succinct zero knowledge proofs, usually called zk-SNARKs ("zero knowledge succinct arguments of knowledge"). A zk-SNARK allows you to generate a proof that some computation has some particular output, in such a way that the proof can be verified extremely quickly even if the underlying computation takes a very long time to run. The "ZK" part adds an additional feature: the proof can keep some of the inputs to the computation hidden.
You can make a proof for the statement "I know a secret number such that if you take the word ‘cow', add the number to the end, and SHA256 hash it 100 million times, the output starts with 0x57d00485aa". The verifier can verify the proof far more quickly than it would take for them to run 100 million hashes themselves, and the proof would also not reveal what the secret number is.
In the context of blockchains, this has two very powerful applications:
- Scalability: if a block takes a long time to verify, one person can verify it and generate a proof, and everyone else can just quickly verify the proof instead
- Privacy: you can prove that you have the right to transfer some asset (you received it, and you didn't already transfer it) without revealing the link to which asset you received. This ensures security without unduly leaking information about who is transacting with whom to the public.
But zk-SNARKs are quite complex; indeed, as recently as in 2014-17 they were still frequently called "moon math". The good news is that since then, the protocols have become simpler and our understanding of them has become much better. This post will try to explain how ZK-SNARKs work, in a way that should be understandable to someone with a medium level of understanding of mathematics.
Why ZK-SNARKs "should" be hard
Let us take the example that we started with: we have a number (we can encode "cow" followed by the secret input as an integer), we take the SHA256 hash of that number, then we do that again another 99,999,999 times, we get the output, and we check what its starting digits are. This is a huge computation.
A "succinct" proof is one where both the size of the proof and the time required to verify it grow much more slowly than the computation to be verified. If we want a "succinct" proof, we cannot require the verifier to do some work per round of hashing (because then the verification time would be proportional to the computation). Instead, the verifier must somehow check the whole computation without peeking into each individual piece of the computation.
One natural technique is random sampling: how about we just have the verifier peek into the computation in 500 different places, check that those parts are correct, and if all 500 checks pass then assume that the rest of the computation must with high probability be fine, too?
Such a procedure could even be turned into a non-interactive proof using the Fiat-Shamir heuristic: the prover computes a Merkle root of the computation, uses the Merkle root to pseudorandomly choose 500 indices, and provides the 500 corresponding Merkle branches of the data. The key idea is that the prover does not know which branches they will need to reveal until they have already "committed to" the data. If a malicious prover tries to fudge the data after learning which indices are going to be checked, that would change the Merkle root, which would result in a new set of random indices, which would require fudging the data again... trapping the malicious prover in an endless cycle.
But unfortunately there is a fatal flaw in naively applying random sampling to spot-check a computation in this way: computation is inherently fragile. If a malicious prover flips one bit somewhere in the middle of a computation, they can make it give a completely different result, and a random sampling verifier would almost never find out.
It only takes one deliberately inserted error, that a random check would almost never catch, to make a computation give a completely incorrect result.
If tasked with the problem of coming up with a zk-SNARK protocol, many people would make their way to this point and then get stuck and give up. How can a verifier possibly check every single piece of the computation, without looking at each piece of the computation individually? There is a clever solution.
see part 2
(Edited)
More on Web3 & Crypto
Isobel Asher Hamilton
3 years ago
$181 million in bitcoin buried in a dump. $11 million to get them back
James Howells lost 8,000 bitcoins. He has $11 million to get them back.
His life altered when he threw out an iPhone-sized hard drive.
Howells, from the city of Newport in southern Wales, had two identical laptop hard drives squirreled away in a drawer in 2013. One was blank; the other had 8,000 bitcoins, currently worth around $181 million.
He wanted to toss out the blank one, but the drive containing the Bitcoin went to the dump.
He's determined to reclaim his 2009 stash.
Howells, 36, wants to arrange a high-tech treasure hunt for bitcoins. He can't enter the landfill.
Newport's city council has rebuffed Howells' requests to dig for his hard drive for almost a decade, stating it would be expensive and environmentally destructive.
I got an early look at his $11 million idea to search 110,000 tons of trash. He expects submitting it to the council would convince it to let him recover the hard disk.
110,000 tons of trash, 1 hard drive
Finding a hard disk among heaps of trash may seem Herculean.
Former IT worker Howells claims it's possible with human sorters, robot dogs, and an AI-powered computer taught to find hard drives on a conveyor belt.
His idea has two versions, depending on how much of the landfill he can search.
His most elaborate solution would take three years and cost $11 million to sort 100,000 metric tons of waste. Scaled-down version costs $6 million and takes 18 months.
He's created a team of eight professionals in AI-powered sorting, landfill excavation, garbage management, and data extraction, including one who recovered Columbia's black box data.
The specialists and their companies would be paid a bonus if they successfully recovered the bitcoin stash.
Howells: "We're trying to commercialize this project."
Howells claimed rubbish would be dug up by machines and sorted near the landfill.
Human pickers and a Max-AI machine would sort it. The machine resembles a scanner on a conveyor belt.
Remi Le Grand of Max-AI told us it will train AI to recognize Howells-like hard drives. A robot arm would select candidates.
Howells has added security charges to his scheme because he fears people would steal the hard drive.
He's budgeted for 24-hour CCTV cameras and two robotic "Spot" canines from Boston Dynamics that would patrol at night and look for his hard drive by day.
Howells said his crew met in May at the Celtic Manor Resort outside Newport for a pitch rehearsal.
Richard Hammond's narrative swings from banal to epic.
Richard Hammond filmed the meeting and created a YouTube documentary on Howells.
Hammond said of Howells' squad, "They're committed and believe in him and the idea."
Hammond: "It goes from banal to gigantic." "If I were in his position, I wouldn't have the strength to answer the door."
Howells said trash would be cleaned and repurposed after excavation. Reburying the rest.
"We won't pollute," he declared. "We aim to make everything better."
After the project is finished, he hopes to develop a solar or wind farm on the dump site. The council is unlikely to accept his vision soon.
A council representative told us, "Mr. Howells can't convince us of anything." "His suggestions constitute a significant ecological danger, which we can't tolerate and are forbidden by our permit."
Will the recovered hard drive work?
The "platter" is a glass or metal disc that holds the hard drive's data. Howells estimates 80% to 90% of the data will be recoverable if the platter isn't damaged.
Phil Bridge, a data-recovery expert who consulted Howells, confirmed these numbers.
If the platter is broken, Bridge adds, data recovery is unlikely.
Bridge says he was intrigued by the proposal. "It's an intriguing case," he added. Helping him get it back and proving everyone incorrect would be a great success story.
Who'd pay?
Swiss and German venture investors Hanspeter Jaberg and Karl Wendeborn told us they would fund the project if Howells received council permission.
Jaberg: "It's a needle in a haystack and a high-risk investment."
Howells said he had no contract with potential backers but had discussed the proposal in Zoom meetings. "Until Newport City Council gives me something in writing, I can't commit," he added.
Suppose he finds the bitcoins.
Howells said he would keep 30% of the data, worth $54 million, if he could retrieve it.
A third would go to the recovery team, 30% to investors, and the remainder to local purposes, including gifting £50 ($61) in bitcoin to each of Newport's 150,000 citizens.
Howells said he opted to spend extra money on "professional firms" to help convince the council.
What if the council doesn't approve?
If Howells can't win the council's support, he'll sue, claiming its actions constitute a "illegal embargo" on the hard drive. "I've avoided that path because I didn't want to cause complications," he stated. I wanted to cooperate with Newport's council.
Howells never met with the council face-to-face. He mentioned he had a 20-minute Zoom meeting in May 2021 but thought his new business strategy would help.
He met with Jessica Morden on June 24. Morden's office confirmed meeting.
After telling the council about his proposal, he can only wait. "I've never been happier," he said. This is our most professional operation, with the best employees.
The "crypto proponent" buys bitcoin every month and sells it for cash.
Howells tries not to think about what he'd do with his part of the money if the hard disk is found functional. "Otherwise, you'll go mad," he added.
This post is a summary. Read the full article here.
ANDREW SINGER
3 years ago
Crypto seen as the ‘future of money’ in inflation-mired countries
Crypto as the ‘future of money' in inflation-stricken nations
Citizens of devalued currencies “need” crypto. “Nice to have” in the developed world.
According to Gemini's 2022 Global State of Crypto report, cryptocurrencies “evolved from what many considered a niche investment into an established asset class” last year.
More than half of crypto owners in Brazil (51%), Hong Kong (51%), and India (54%), according to the report, bought cryptocurrency for the first time in 2021.
The study found that inflation and currency devaluation are powerful drivers of crypto adoption, especially in emerging market (EM) countries:
“Respondents in countries that have seen a 50% or greater devaluation of their currency against the USD over the last decade were more than 5 times as likely to plan to purchase crypto in the coming year.”
Between 2011 and 2021, the real lost 218 percent of its value against the dollar, and 45 percent of Brazilians surveyed by Gemini said they planned to buy crypto in 2019.
The rand (South Africa's currency) has fallen 103 percent in value over the last decade, second only to the Brazilian real, and 32 percent of South Africans expect to own crypto in the coming year. Mexico and India, the third and fourth highest devaluation countries, followed suit.
Compared to the US dollar, Hong Kong and the UK currencies have not devalued in the last decade. Meanwhile, only 5% and 8% of those surveyed in those countries expressed interest in buying crypto.
What can be concluded? Noah Perlman, COO of Gemini, sees various crypto use cases depending on one's location.
‘Need to have' investment in countries where the local currency has devalued against the dollar, whereas in the developed world it is still seen as a ‘nice to have'.
Crypto as money substitute
As an adjunct professor at New York University School of Law, Winston Ma distinguishes between an asset used as an inflation hedge and one used as a currency replacement.
Unlike gold, he believes Bitcoin (BTC) is not a “inflation hedge”. They acted more like growth stocks in 2022. “Bitcoin correlated more closely with the S&P 500 index — and Ether with the NASDAQ — than gold,” he told Cointelegraph. But in the developing world, things are different:
“Inflation may be a primary driver of cryptocurrency adoption in emerging markets like Brazil, India, and Mexico.”
According to Justin d'Anethan, institutional sales director at the Amber Group, a Singapore-based digital asset firm, early adoption was driven by countries where currency stability and/or access to proper banking services were issues. Simply put, he said, developing countries want alternatives to easily debased fiat currencies.
“The larger flows may still come from institutions and developed countries, but the actual users may come from places like Lebanon, Turkey, Venezuela, and Indonesia.”
“Inflation is one of the factors that has and continues to drive adoption of Bitcoin and other crypto assets globally,” said Sean Stein Smith, assistant professor of economics and business at Lehman College.
But it's only one factor, and different regions have different factors, says Stein Smith. As a “instantaneously accessible, traceable, and cost-effective transaction option,” investors and entrepreneurs increasingly recognize the benefits of crypto assets. Other places promote crypto adoption due to “potential capital gains and returns”.
According to the report, “legal uncertainty around cryptocurrency,” tax questions, and a general education deficit could hinder adoption in Asia Pacific and Latin America. In Africa, 56% of respondents said more educational resources were needed to explain cryptocurrencies.
Not only inflation, but empowering our youth to live better than their parents without fear of failure or allegiance to legacy financial markets or products, said Monica Singer, ConsenSys South Africa lead. Also, “the issue of cash and remittances is huge in Africa, as is the issue of social grants.”
Money's future?
The survey found that Brazil and Indonesia had the most cryptocurrency ownership. In each country, 41% of those polled said they owned crypto. Only 20% of Americans surveyed said they owned cryptocurrency.
These markets are more likely to see cryptocurrencies as the future of money. The survey found:
“The majority of respondents in Latin America (59%) and Africa (58%) say crypto is the future of money.”
Brazil (66%), Nigeria (63%), Indonesia (61%), and South Africa (57%). Europe and Australia had the fewest believers, with Denmark at 12%, Norway at 15%, and Australia at 17%.
Will the Ukraine conflict impact adoption?
The poll was taken before the war. Will the devastating conflict slow global crypto adoption growth?
With over $100 million in crypto donations directly requested by the Ukrainian government since the war began, Stein Smith says the war has certainly brought crypto into the mainstream conversation.
“This real-world demonstration of decentralized money's power could spur wider adoption, policy debate, and increased use of crypto as a medium of exchange.”
But the war may not affect all developing nations. “The Ukraine war has no impact on African demand for crypto,” Others loom larger. “Yes, inflation, but also a lack of trust in government in many African countries, and a young demographic very familiar with mobile phones and the internet.”
A major success story like Mpesa in Kenya has influenced the continent and may help accelerate crypto adoption. Creating a plan when everyone you trust fails you is directly related to the African spirit, she said.
On the other hand, Ma views the Ukraine conflict as a sort of crisis check for cryptocurrencies. For those in emerging markets, the Ukraine-Russia war has served as a “stress test” for the cryptocurrency payment rail, he told Cointelegraph.
“These emerging markets may see the greatest future gains in crypto adoption.”
Inflation and currency devaluation are persistent global concerns. In such places, Bitcoin and other cryptocurrencies are now seen as the “future of money.” Not in the developed world, but that could change with better regulation and education. Inflation and its impact on cash holdings are waking up even Western nations.
Read original post here.
Yogesh Rawal
3 years ago
Blockchain to solve growing privacy challenges
Most online activity is now public. Businesses collect, store, and use our personal data to improve sales and services.
In 2014, Uber executives and employees were accused of spying on customers using tools like maps. Another incident raised concerns about the use of ‘FaceApp'. The app was created by a small Russian company, and the photos can be used in unexpected ways. The Cambridge Analytica scandal exposed serious privacy issues. The whole incident raised questions about how governments and businesses should handle data. Modern technologies and practices also make it easier to link data to people.
As a result, governments and regulators have taken steps to protect user data. The General Data Protection Regulation (GDPR) was introduced by the EU to address data privacy issues. The law governs how businesses collect and process user data. The Data Protection Bill in India and the General Data Protection Law in Brazil are similar.
Despite the impact these regulations have made on data practices, a lot of distance is yet to cover.
Blockchain's solution
Blockchain may be able to address growing data privacy concerns. The technology protects our personal data by providing security and anonymity. The blockchain uses random strings of numbers called public and private keys to maintain privacy. These keys allow a person to be identified without revealing their identity. Blockchain may be able to ensure data privacy and security in this way. Let's dig deeper.
Financial transactions
Online payments require third-party services like PayPal or Google Pay. Using blockchain can eliminate the need to trust third parties. Users can send payments between peers using their public and private keys without providing personal information to a third-party application. Blockchain will also secure financial data.
Healthcare data
Blockchain technology can give patients more control over their data. There are benefits to doing so. Once the data is recorded on the ledger, patients can keep it secure and only allow authorized access. They can also only give the healthcare provider part of the information needed.
The major challenge
We tried to figure out how blockchain could help solve the growing data privacy issues. However, using blockchain to address privacy concerns has significant drawbacks. Blockchain is not designed for data privacy. A ‘distributed' ledger will be used to store the data. Another issue is the immutability of blockchain. Data entered into the ledger cannot be changed or deleted. It will be impossible to remove personal data from the ledger even if desired.
MIT's Enigma Project aims to solve this. Enigma's ‘Secret Network' allows nodes to process data without seeing it. Decentralized applications can use Secret Network to use encrypted data without revealing it.
Another startup, Oasis Labs, uses blockchain to address data privacy issues. They are working on a system that will allow businesses to protect their customers' data.
Conclusion
Blockchain technology is already being used. Several governments use blockchain to eliminate centralized servers and improve data security. In this information age, it is vital to safeguard our data. How blockchain can help us in this matter is still unknown as the world explores the technology.
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Maddie Wang
3 years ago
Easiest and fastest way to test your startup idea!
Here's the fastest way to validate company concepts.
I squandered a year after dropping out of Stanford designing a product nobody wanted.
But today, I’m at 100k!
Differences:
I was designing a consumer product when I dropped out.
I coded MVP, got 1k users, and got YC interview.
Nice, huh?
WRONG!
Still coding and getting users 12 months later
WOULD PEOPLE PAY FOR IT? was the riskiest assumption I hadn't tested.
When asked why I didn't verify payment, I said,
Not-ready products. Now, nobody cares. The website needs work. Include this. Increase usage…
I feared people would say no.
After 1 year of pushing it off, my team told me they were really worried about the Business Model. Then I asked my audience if they'd buy my product.
So?
No, overwhelmingly.
I felt like I wasted a year building a product no one would buy.
Founders Cafe was the opposite.
Before building anything, I requested payment.
40 founders were interviewed.
Then we emailed Stanford, YC, and other top founders, asking them to join our community.
BOOM! 10/12 paid!
Without building anything, in 1 day I validated my startup's riskiest assumption. NOT 1 year.
Asking people to pay is one of the scariest things.
I understand.
I asked Stanford queer women to pay before joining my gay sorority.
I was afraid I'd turn them off or no one would pay.
Gay women, like those founders, were in such excruciating pain that they were willing to pay me upfront to help.
You can ask for payment (before you build) to see if people have the burning pain. Then they'll pay!
Examples from Founders Cafe members:
😮 Using a fake landing page, a college dropout tested a product. Paying! He built it and made $3m!
😮 YC solo founder faked a Powerpoint demo. 5 Enterprise paid LOIs. $1.5m raised, built, and in YC!
😮 A Harvard founder can convert Figma to React. 1 day, 10 customers. Built a tool to automate Figma -> React after manually fulfilling requests. 1m+
Bad example:
😭 Stanford Dropout Spends 1 Year Building Product Without Payment Validation
Some people build for a year and then get paying customers.
What I'm sharing is my experience and what Founders Cafe members have told me about validating startup ideas.
Don't waste a year like I did.
After my first startup failed, I planned to re-enroll at Stanford/work at Facebook.
After people paid, I quit for good.
I've hit $100k!
Hope this inspires you to request upfront payment! It'll change your life
Teronie Donalson
3 years ago
The best financial advice I've ever received and how you can use it.
Taking great financial advice is key to financial success.
A wealthy man told me to INVEST MY MONEY when I was young.
As I entered Starbucks, an older man was leaving. I noticed his watch and expensive-looking shirt, not like the guy in the photo, but one made of fine fabric like vicuna wool, which can only be shorn every two to three years. His Bentley confirmed my suspicions about his wealth.
This guy looked like James Bond, so I asked him how to get rich like him.
"Drug dealer?" he laughed.
Whether he was telling the truth, I'll never know, and I didn't want to be an accessory, but he quickly added, "Kid, invest your money; it will do wonders." He left.
When he told me to invest, he didn't say what. Later, I realized the investment game has so many levels that even if he drew me a blueprint, I wouldn't understand it.
The best advice I received was to invest my earnings. I must decide where to invest.
I'll preface by saying I'm not a financial advisor or Your financial advisor, but I'll share what I've learned from books, links, and sources. The rest is up to you.
Basically:
Invest your Money
Money is money, whether you call it cake, dough, moolah, benjamins, paper, bread, etc.
If you're lucky, you can buy one of the gold shirts in the photo.
Investing your money today means putting it towards anything that could be profitable.
According to the website Investopedia:
“Investing is allocating money to generate income or profit.”
You can invest in a business, real estate, or a skill that will pay off later.
Everyone has different goals and wants at different stages of life, so investing varies.
He was probably a sugar daddy with his Bentley, nice shirt, and Rolex.
In my twenties, I started making "good" money; now, in my forties, with a family and three kids, I'm building a legacy for my grandkids.
“It’s not how much money you make, but how much money you keep, how hard it works for you, and how many generations you keep it for.” — Robert Kiyosaki.
Money isn't evil, but lack of it is.
Financial stress is a major source of problems, according to studies.
Being broke hurts, especially if you want to provide for your family or do things.
“An investment in knowledge pays the best interest.” — Benjamin Franklin.
Investing in knowledge is invaluable. Before investing, do your homework.
You probably didn't learn about investing when you were young, like I didn't. My parents were in survival mode, making investing difficult.
In my 20s, I worked in banking to better understand money.
So, why invest?
Growth requires investment.
Investing puts money to work and can build wealth. Your money may outpace inflation with smart investing. Compounding and the risk-return tradeoff boost investment growth.
Investing your money means you won't have to work forever — unless you want to.
Two common ways to make money are;
-working hard,
and
-interest or capital gains from investments.
Capital gains can help you invest.
“How many millionaires do you know who have become wealthy by investing in savings accounts? I rest my case.” — Robert G. Allen
If you keep your money in a savings account, you'll earn less than 2% interest at best; the bank makes money by loaning it out.
Savings accounts are a safe bet, but the low-interest rates limit your gains.
Don't skip it. An emergency fund should be in a savings account, not the market.
Other reasons to invest:
Investing can generate regular income.
If you own rental properties, the tenant's rent will add to your cash flow.
Daily, weekly, or monthly rentals (think Airbnb) generate higher returns year-round.
Capital gains are taxed less than earned income if you own dividend-paying or appreciating stock.
Time is on your side
“Compound interest is the eighth wonder of the world. He who understands it, earns it; he who doesn’t — pays it.” — Albert Einstein
Historical data shows that young investors outperform older investors. So you can use compound interest over decades instead of investing at 45 and having less time to earn.
If I had taken that man's advice and invested in my twenties, I would have made a decent return by my thirties. (Depending on my investments)
So for those who live a YOLO (you only live once) life, investing can't hurt.
Investing increases your knowledge.
Lessons are clearer when you're invested. Each win boosts confidence and draws attention to losses. Losing money prompts you to investigate.
Before investing, I read many financial books, but I didn't understand them until I invested.
Now what?
What do you invest in? Equities, mutual funds, ETFs, retirement accounts, savings, business, real estate, cryptocurrencies, marijuana, insurance, etc.
The key is to start somewhere. Know you don't know everything. You must care.
“A journey of a thousand miles must begin with a single step.” — Lao Tzu.
Start simple because there's so much information. My first investment book was:
Robert Kiyosaki's "Rich Dad, Poor Dad"
This easy-to-read book made me hungry for more. This book is about the money lessons rich parents teach their children, which poor and middle-class parents neglect. The poor and middle-class work for money, while the rich let their assets work for them, says Kiyosaki.
There is so much to learn, but you gotta start somewhere.
More books:
***Wisdom
I hope I'm not suggesting that investing makes everything rosy. Remember three rules:
1. Losing money is possible.
2. Losing money is possible.
3. Losing money is possible.
You can lose money, so be careful.
Read, research, invest.
Golden rules for Investing your money
Never invest money you can't lose.
Financial freedom is possible regardless of income.
"Courage taught me that any sound investment will pay off, no matter how bad a crisis gets." Helu Carlos
"I'll tell you Wall Street's secret to wealth. When others are afraid, you're greedy. You're afraid when others are greedy. Buffett
Buy low, sell high, and have an exit strategy.
Ask experts or wealthy people for advice.
"With a good understanding of history, we can have a clear vision of the future." Helu Carlos
"It's not whether you're right or wrong, but how much money you make when you're right." Soros
"The individual investor should act as an investor, not a speculator." Graham
"It's different this time" is the most dangerous investment phrase. Templeton
Lastly,
Avoid quick-money schemes. Building wealth takes years, not months.
Start small and work your way up.
Thanks for reading!
This post is a summary. Read the full article here
Zuzanna Sieja
3 years ago
In 2022, each data scientist needs to read these 11 books.
Non-technical talents can benefit data scientists in addition to statistics and programming.
As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.
Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.
Ready? Let’s dive in.
Best books for data scientists
1. The Black Swan
Author: Nassim Taleb
First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.
Three characteristics define a black swan event:
It is erratic.
It has a significant impact.
Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.
People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.
Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.
Try multiple tactics and models because you may find the answer.
2. High Output Management
Author: Andrew Grove
Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.
That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.
Five lessons:
Every action is a procedure.
Meetings are a medium of work
Manage short-term goals in accordance with long-term strategies.
Mission-oriented teams accelerate while functional teams increase leverage.
Utilize performance evaluations to enhance output.
So — if the above captures your imagination, it’s well worth getting stuck in.
3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers
Author: Ben Horowitz
Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.
Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.
It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.
Find suggestions on:
create software
Run a business.
Promote a product
Obtain resources
Smart investment
oversee daily operations
This book will help you cope with tough times.
4. Obviously Awesome: How to Nail Product Positioning
Author: April Dunford
Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.
How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.
You'll learn:
Select the ideal market for your products.
Connect an audience to the value of your goods right away.
Take use of three positioning philosophies.
Utilize market trends to aid purchasers
5. The Mom test
Author: Rob Fitzpatrick
The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.
Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.
6. Introduction to Machine Learning with Python: A Guide for Data Scientists
Authors: Andreas C. Müller, Sarah Guido
Now, technical documents.
This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.
Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.
If you know machine learning or artificial neural networks, skip this.
7. Python Data Science Handbook: Essential Tools for Working with Data
Author: Jake VanderPlas
Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.
Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.
The only thing missing is a way to apply your learnings.
8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Author: Wes McKinney
The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.
The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.
9. Data Science from Scratch
Author: Joel Grus
Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.
The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.
Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.
10. Machine Learning Yearning
Author: Andrew Ng
Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.
The book delivers knowledge and teaches how to apply it, so you'll know how to:
Determine the optimal course of action for your ML project.
Create software that is more effective than people.
Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.
Identifying machine learning system flaws
Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.
11. Deep Learning with PyTorch Step-by-Step
Author: Daniel Voigt Godoy
The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.
It comprises four parts:
Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.
Is every data scientist a humanist?
Even as a technological professional, you can't escape human interaction, especially with clients.
We hope these books will help you develop interpersonal skills.