More on Personal Growth
James White
2 years ago
Three Books That Can Change Your Life in a Day
I've summarized each.
Anne Lamott said books are important. Books help us understand ourselves and our behavior. They teach us about community, friendship, and death.
I read. One of my few life-changing habits. 100+ books a year improve my life. I'll list life-changing books you can read in a day. I hope you like them too.
Let's get started!
1) Seneca's Letters from a Stoic
One of my favorite philosophy books. Ryan Holiday, Naval Ravikant, and other prolific readers recommend it.
Seneca wrote 124 letters at the end of his life after working for Nero. Death, friendship, and virtue are discussed.
It's worth rereading. When I'm in trouble, I consult Seneca.
It's brief. The book could be read in one day. However, use it for guidance during difficult times.
My favorite book quotes:
Many men find that becoming wealthy only alters their problems rather than solving them.
You will never be poor if you live in harmony with nature; you will never be wealthy if you live according to what other people think.
We suffer more frequently in our imagination than in reality; there are more things that are likely to frighten us than to crush us.
2) Steven Pressfield's book The War of Art
I’ve read this book twice. I'll likely reread it before 2022 is over.
The War Of Art is the best productivity book. Steven offers procrastination-fighting tips.
Writers, musicians, and creative types will love The War of Art. Workplace procrastinators should also read this book.
My favorite book quotes:
The act of creation is what matters most in art. Other than sitting down and making an effort every day, nothing else matters.
Working creatively is not a selfish endeavor or an attempt by the actor to gain attention. It serves as a gift for all living things in the world. Don't steal your contribution from us. Give us everything you have.
Fear is healthy. Fear is a signal, just like self-doubt. Fear instructs us on what to do. The more terrified we are of a task or calling, the more certain we can be that we must complete it.
3) Darren Hardy's The Compound Effect
The Compound Effect offers practical tips to boost productivity by 10x.
The author believes each choice shapes your future. Pizza may seem harmless. However, daily use increases heart disease risk.
Positive outcomes too. Daily gym visits improve fitness. Reading an hour each night can help you learn. Writing 1,000 words per day would allow you to write a novel in under a year.
Your daily choices affect compound interest and your future. Thus, better habits can improve your life.
My favorite book quotes:
Until you alter a daily habit, you cannot change your life. The key to your success can be found in the actions you take each day.
The hundreds, thousands, or millions of little things are what distinguish the ordinary from the extraordinary; it is not the big things that add up in the end.
Don't worry about willpower. Time to use why-power. Only when you relate your decisions to your aspirations and dreams will they have any real meaning. The decisions that are in line with what you define as your purpose, your core self, and your highest values are the wisest and most inspiring ones. To avoid giving up too easily, you must want something and understand why you want it.
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.
Datt Panchal
3 years ago
The Learning Habit
The Habit of Learning implies constantly learning something new. One daily habit will make you successful. Learning will help you succeed.
Most successful people continually learn. Success requires this behavior. Daily learning.
Success loves books. Books offer expert advice. Everything is online today. Most books are online, so you can skip the library. You must download it and study for 15-30 minutes daily. This habit changes your thinking.
Typical Successful People
Warren Buffett reads 500 pages of corporate reports and five newspapers for five to six hours each day.
Each year, Bill Gates reads 50 books.
Every two weeks, Mark Zuckerberg reads at least one book.
According to his brother, Elon Musk studied two books a day as a child and taught himself engineering and rocket design.
Learning & Making Money Online
No worries if you can't afford books. Everything is online. YouTube, free online courses, etc.
How can you create this behavior in yourself?
1) Consider what you want to know
Before learning, know what's most important. So, move together.
Set a goal and schedule learning.
After deciding what you want to study, create a goal and plan learning time.
3) GATHER RESOURCES
Get the most out of your learning resources. Online or offline.
You might also like
Emma Jade
3 years ago
6 hacks to create content faster
Content gurus' top time-saving hacks.
I'm a content strategist, writer, and graphic designer. Time is more valuable than money.
Money is always available. Even if you're poor. Ways exist.
Time is passing, and one day we'll run out.
Sorry to be morbid.
In today's digital age, you need to optimize how you create content for your organization. Here are six content creation hacks.
1. Use templates
Use templates to streamline your work whether generating video, images, or documents.
Setup can take hours. Using a free resource like Canva, you can create templates for any type of material.
This will save you hours each month.
2. Make a content calendar
You post without a plan? A content calendar solves 50% of these problems.
You can prepare, organize, and plan your material ahead of time so you're not scrambling when you remember, "Shit, it's Mother's Day!"
3. Content Batching
Batching content means creating a lot in one session. This is helpful for video content that requires a lot of setup time.
Batching monthly content saves hours. Time is a valuable resource.
When working on one type of task, it's easy to get into a flow state. This saves time.
4. Write Caption
On social media, we generally choose the image first and then the caption. Writing captions first sometimes work better, though.
Writing the captions first can allow you more creative flexibility and be easier if you're not excellent with language.
Say you want to tell your followers something interesting.
Writing a caption first is easier than choosing an image and then writing a caption to match.
Not everything works. You may have already-created content that needs captioning. When you don't know what to share, think of a concept, write the description, and then produce a video or graphic.
Cats can be skinned in several ways..
5. Repurpose
Reuse content when possible. You don't always require new stuff. In fact, you’re pretty stupid if you do #SorryNotSorry.
Repurpose old content. All those blog entries, videos, and unfinished content on your desk or hard drive.
This blog post can be turned into a social media infographic. Canva's motion graphic function can animate it. I can record a YouTube video regarding this issue for a podcast. I can make a post on each point in this blog post and turn it into an eBook or paid course.
And it doesn’t stop there.
My point is, to think outside the box and really dig deep into ways you can leverage the content you’ve already created.
6. Schedule Them
If you're still manually posting content, get help. When you batch your content, schedule it ahead of time.
Some scheduling apps are free or cheap. No excuses.
Don't publish and ghost.
Scheduling saves time by preventing you from doing it manually. But if you never engage with your audience, the algorithm won't reward your material.
Be online and engage your audience.
Content Machine
Use these six content creation hacks. They help you succeed and save time.
Vitalik
3 years ago
An approximate introduction to how zk-SNARKs are possible (part 2)
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? But it turns out that there is a clever solution.
Polynomials
Polynomials are a special class of algebraic expressions of the form:
- x+5
- x^4
- x^3+3x^2+3x+1
- 628x^{271}+318x^{270}+530x^{269}+…+69x+381
i.e. they are a sum of any (finite!) number of terms of the form cx^k
There are many things that are fascinating about polynomials. But here we are going to zoom in on a particular one: polynomials are a single mathematical object that can contain an unbounded amount of information (think of them as a list of integers and this is obvious). The fourth example above contained 816 digits of tau, and one can easily imagine a polynomial that contains far more.
Furthermore, a single equation between polynomials can represent an unbounded number of equations between numbers. For example, consider the equation A(x)+ B(x) = C(x). If this equation is true, then it's also true that:
- A(0)+B(0)=C(0)
- A(1)+B(1)=C(1)
- A(2)+B(2)=C(2)
- A(3)+B(3)=C(3)
And so on for every possible coordinate. You can even construct polynomials to deliberately represent sets of numbers so you can check many equations all at once. For example, suppose that you wanted to check:
- 12+1=13
- 10+8=18
- 15+8=23
- 15+13=28
You can use a procedure called Lagrange interpolation to construct polynomials A(x) that give (12,10,15,15) as outputs at some specific set of coordinates (eg. (0,1,2,3)), B(x) the outputs (1,8,8,13) on thos same coordinates, and so forth. In fact, here are the polynomials:
- A(x)=-2x^3+\frac{19}{2}x^2-\frac{19}{2}x+12
- B(x)=2x^3-\frac{19}{2}x^2+\frac{29}{2}x+1
- C(x)=5x+13
Checking the equation A(x)+B(x)=C(x) with these polynomials checks all four above equations at the same time.
Comparing a polynomial to itself
You can even check relationships between a large number of adjacent evaluations of the same polynomial using a simple polynomial equation. This is slightly more advanced. Suppose that you want to check that, for a given polynomial F, F(x+2)=F(x)+F(x+1) with the integer range {0,1…89} (so if you also check F(0)=F(1)=1, then F(100) would be the 100th Fibonacci number)
As polynomials, F(x+2)-F(x+1)-F(x) would not be exactly zero, as it could give arbitrary answers outside the range x={0,1…98}. But we can do something clever. In general, there is a rule that if a polynomial P is zero across some set S=\{x_1,x_2…x_n\} then it can be expressed as P(x)=Z(x)*H(x), where Z(x)=(x-x_1)*(x-x_2)*…*(x-x_n) and H(x) is also a polynomial. In other words, any polynomial that equals zero across some set is a (polynomial) multiple of the simplest (lowest-degree) polynomial that equals zero across that same set.
Why is this the case? It is a nice corollary of polynomial long division: the factor theorem. We know that, when dividing P(x) by Z(x), we will get a quotient Q(x) and a remainder R(x) is strictly less than that of Z(x). Since we know that P is zero on all of S, it means that R has to be zero on all of S as well. So we can simply compute R(x) via polynomial interpolation, since it's a polynomial of degree at most n-1 and we know n values (the zeros at S). Interpolating a polynomial with all zeroes gives the zero polynomial, thus R(x)=0 and H(x)=Q(x).
Going back to our example, if we have a polynomial F that encodes Fibonacci numbers (so F(x+2)=F(x)+F(x+1) across x=\{0,1…98\}), then I can convince you that F actually satisfies this condition by proving that the polynomial P(x)=F(x+2)-F(x+1)-F(x) is zero over that range, by giving you the quotient:
H(x)=\frac{F(x+2)-F(x+1)-F(x)}{Z(x)}
Where Z(x) = (x-0)*(x-1)*…*(x-98).
You can calculate Z(x) yourself (ideally you would have it precomputed), check the equation, and if the check passes then F(x) satisfies the condition!
Now, step back and notice what we did here. We converted a 100-step-long computation into a single equation with polynomials. Of course, proving the N'th Fibonacci number is not an especially useful task, especially since Fibonacci numbers have a closed form. But you can use exactly the same basic technique, just with some extra polynomials and some more complicated equations, to encode arbitrary computations with an arbitrarily large number of steps.
see part 3
Peter Steven Ho
3 years ago
Thank You for 21 Fantastic Years, iPod
Apple's latest revelation may shock iPod fans and former owners.
Apple discontinued the iPod touch on May 11, 2022. After 21 years, Apple killed the last surviving iPod, a device Steve Jobs believed would revolutionize the music industry.
Jobs was used to making bold predictions, but few expected Apple's digital music player to change the music industry. It did.
This chaos created new business opportunities. Spotify, YouTube, and Amazon are products of that chaotic era.
As the digital landscape changes, so do consumers, and the iPod has lost favor. I'm sure Apple realizes the importance of removing an icon. The iPod was Apple like the Mac and iPhone. I think it's bold to retire such a key Apple cornerstone. What would Jobs do?
iPod evolution across the ages
Here's an iPod family tree for all you enthusiasts.
iPod vintage (Oct 2001 to Sep 2014, 6 generations)
The original iPod had six significant upgrades since 2001. Apple announced an 80 GB ($249) and 160 GB ($349) iPod classic in 2007.
Apple updated the 80 GB model with a 120 GB device in September 2008. Apple upgraded the 120 GB model with a 160 GB variant a year later (2009). This was the last iteration, and Apple discontinued the classic in September 2014.
iPod nano (Jan 2004 to Sep 2005, 2 generations)
Apple debuted a smaller, brightly-colored iPod in 2004. The first model featured 4 GB, enough for 1,000 songs.
Apple produced a new 4 GB or 6 GB iPod mini in February 2005 and discontinued it in September when they released a better-looking iPod nano.
iTouch nano (Sep 2005 to July 2017, 7 generations)
I loved the iPod nano. It was tiny and elegant with enough tech to please most music aficionados, unless you carry around your complete music collection.
Apple owed much of the iPod nano's small form and success to solid-state flash memory. Flash memory doesn't need power because it has no moving parts. This makes the iPod nano more durable than the iPod classic and mini, which employ hard drives.
Apple manufactured seven generations of the iPod nano, improving its design, display screen, memory, battery, and software, but abandoned it in July 2017 due to dwindling demand.
Shuffle iPod (Jan 2005 to Jul 2017, 4 generations)
The iPod shuffle was entry-level. It was a simple, lightweight, tiny music player. The iPod shuffle was perfect for lengthy bike trips, runs, and hikes.
Apple sold 10 million iPod shuffles in the first year and kept making them for 12 years, through four significant modifications.
iOS device (Sep 2007 to May 2022, 7 generations)
The iPod touch's bigger touchscreen interface made it a curious addition to the iPod family. The iPod touch resembled an iPhone more than the other iPods, making them hard to tell apart.
Many were dissatisfied that Apple removed functionality from the iPod touch to avoid making it too similar to the iPhone. Seven design improvements over 15 years brought the iPod touch closer to the iPhone, but not completely.
The iPod touch uses the same iOS operating system as the iPhone, giving it access to many apps, including handheld games.
The iPod touch's long production run is due to the next generation of music-loving gamers.
What made the iPod cool
iPod revolutionized music listening. It was the first device to store and play MP3 music, allowing you to carry over 1,000 songs anywhere.
The iPod changed consumer electronics with its scroll wheel and touchscreen. Jobs valued form and function equally. He showed people that a product must look good to inspire an emotional response and ignite passion.
The elegant, tiny iPod was a tremendous sensation when it arrived for $399 in October 2001. Even at this price, it became a must-have for teens to CEOs.
It's hard to identify any technology that changed how music was downloaded and played like the iPod. Apple iPod and iTunes had 63% of the paid music download market in the fourth quarter of 2012.
The demise of the iPod was inevitable
Apple discontinuing the iPod touch after 21 years is sad. This ends a 00s music icon.
Jobs was a genius at anticipating market needs and opportunities, and Apple launched the iPod at the correct time.
Few consumer electronics items have had such a lasting impact on music lovers and the music industry as the iPod.
Smartphones and social media have contributed to the iPod's decline. Instead of moving to the music, the new generation of consumers is focused on social media. They're no longer passive content consumers; they're active content creators seeking likes and followers. Here, the smartphone has replaced the iPod.
It's hard not to feel a feeling of loss, another part of my adolescence now forgotten by the following generation.
So, if you’re lucky enough to have a working iPod, hang on to that relic and enjoy the music and the nostalgia.