Month 1 - Reset phase

Overview of the month.

First of all, to reset our minds we need to understand how the brain works and how we can learn more efficiently. To do this we did a course called Learning how to Learn a read a book called Make it stick. Here are some notes that I recovered from this. Then a good story about computers that I'm going to share with you. Finally the summary of the month and that's all.

Learning how to learn

I followed this course and I'm going to share some of my maps and thoughts.

Week 1

What is learning?

To learn more effectively you should use both
You can activate diffused mode by taking a break, going out for a walk, etc.
We need both sides.
Subconscious is smarter than conscious.
Trying to understand something complicated is hard.

We can use pomodoro techinique to avoid procrastination.
Practice makes permanent, be carful with that we dont want everything to be permanent.
Long term and working memory. Long term is a warehouse and working is blackboard. You have to decide where you want to store things.
Sleeping is important. You also think while you sleep.

Week 2

We can learn things faster and easier by chunking.

To chunk something you have to focus on what you are learning.
Understanding the basic ideas is like glue, like little pieces that can connect later on.
Chunks, knowledge (How something works, what is something, how to use it).
Context where to use it.
Chunking may involve your learning how to use a certain problem solving technique.
Chunks are best built with focused attention.

Sometimes we fool ourselves.

Recalling is the best way to learn something.
Test yourself.
Interleaving you study sessions is also helpful.

Now that we have chunks we can build a bigger image.

Chunks are a library.
Diffused mode helps us connecting the chunks to solve a problem.

Week 3

We can do more things to avoid procrastination.

Being in zombie mode helps saving energy.
Make things you don't like a habit.
You have to avoid distractors.
Having a TODO list will help your mind to process all the information.
Write your plan tasks out the night before so your brain has time to dwell on your goals and help ensure success.
Reward the zombie.
Have backup plans for when you procrastinate.
The cue an event which triggers routine, reward and belief.
To make a good habit, create a cue, make a routine around it, give yourself a reward if you follow through and you’ll start forming a belief about you being the type of person who has the good habit.

We also need to be aware of the memory.

Write down a list of things to do so you have your memory clean.
Putting things down means they’re out of your head and you can devote all of your brainpower to focused thinking rather than worrying about what it was you had to do later.
Finish time is also important.
Master something, in this way will occupy less space in your working memory.
Repetition over several days is really helpful.
The memory palace technique, placing memorable images in a scene that's familiar to you allows you to dip into the strength of your visual memory system, providing a particularly powerful way of grouping things you want to remember.

Week 4

Physical exercise helps new neurons survive.
Physical exercise helps you learn.
If you believe you can’t learn something, you’re right.
If you believe you can learn something, you’re right.
You don't have be a genius. Learning takes time.

Understand concepts in many different fields is to create a metaphor or analogy for them.
Every genius starts from somewhere.
Studying with friends, you can more easily catch where your thinking has gone astray.
Whenever possible, you should blink, shift your attention, and then double-check if what you are doing is correct.

Make It Stick

I read the book and has a lot of examples and results of studies but I'm going to point out only the most important ideas.

Learning is Misunderstood.

Just to discuss and point out the most important points of chapter one. Most of the chapter is about research findings. The main point of this chapter is to address many of the common misunderstandings about learning.

First, people think that we are going to learn better if we make it easy and faster and the truth is that learning is hard and it takes time. We have some points here:

Learning is deeper and more durable when it's effortful
Trying to solve a problem before being taught the solution leads to a better understanding
All learning requires a foundation of prior knowledge.

Rereading is not always the best option. Not because you remember or words or text appears to be clearer it doesn't mean you learn it. These are illusions of knowing we can instead do the following:

Do retrieval practice.
Low-stakes quizzing or self-testing.
Space our practices.
Interleave the practices.
Try to solve a problem without knowing the solution.
Get the main ideas of a text.

Finally, to close the chapter, the author makes us remember that mastery requires both possession of ready knowledge and the conceptual understanding of how to use it. What I can understand is that is going to take some time, and that we gain mastery in a subject through practice and time.

To Learn Retrieve

In chapter two the author highlighted the importance of retrieval practice. Starts the chapter with a history of a surgeon who had a hard case and he manages to solve everything using his knowledge, experience, and reflection. The important thing here is that he could do this by retrieving things from his head and the author suggests that we should do this to have deeper learning in anything.

That's why in this chapter we see a lot of findings of studies that suggest that retrieval practice is the best way to learn things. I kind of get the main ideas:

We lose 70 percent of what we just heard or read, so we have to stop the forgetting process.
Retrieval must be repeated again and again this helps to consolidate representations in the brain.
When we space retrieval practice leads to stronger retention.
We can do retrieval practices by doing a test or testing ourselves.
We can do quizzes or flashcards.
If we have feedback produces better learning.

This has shown a lot of positive results and this what we can do.

Mix up your practice.

In this chapter what I get most is that we don't learn all the things the same way, so is an error trying to approach everything the same way. We keep believing that massed practices are the best way of learning things but it's an illusion because you are going to forget things because are not well consolidated.

The invitation of the author is to make our practice sessions different, we don't have to do the same things in each session. I liked this part because is true! By doing this you can see things in a different way which also creates knowledge that we can transfer to different situations. That's the point of learning things.

The other invitation is to interleave our practices and the part that I most remember is that in that interleave space you forget something then you have to relearn it and this makes knowledge deeper. This creates something called Learning from experience. Learning from experience takes us to another level in which we have to do a deeper reflection. Some questions in our reflection could be:

What happened?
What did I do?
How did it work out?

I think this is the best way to learn something and take it with us, this is how things stick with us, in my opinion.

Embrace Difficulties.

I just want to stick with some main ideas of the book:

Learning is a three-step process.
Encoding: Information is held in the working memory before being consolidated into a cohesive representation of knowledge.
Consolidation: reorganizes and stabilizes memory traces, makes a connection to past experiences.
Retrieval updates learning and enable you to apply it when need it.
Cues can reactivate the memories.
Repeated effortful recall or practice integrate into mental models.
There are also some recommendations for conceptual retrieval practice.
Generation: Solve a problem when no solution.
Reflection: Reflection is about making questions.
Elaboration: Elaborations is about taking the main ideas and taking them into our words.

My reflection of this chapter is that learning doesn't have to be easy, (like anything in this life) we need time and effort to learn things. First, we need to understand how we learn and consolidate things in our brain or memory and the way we do it is by getting an idea or concept and try to put it in our heads then avoid forgetting. Another thing that I agree with is that sometimes you have to dare to do things that you don't know how and that's how you enter a new process in which you have to use your past experiences and creativity and reflect and elaborate and in my opinion, this is the best way generate deeper learning.

Avoid illusions of knowing.

What I understood from this chapter was that we have 2 have two analytic systems and one of those uses memory and experiences. This is important because we tend to trust that system a lot and feel very comfortable with that. We first, need to realize that memory could not always work properly, sometimes plays tricks and we need to have that in mind because what we think or retrieve from our memories might not be true.

Having that in mind we can't trust 100 percent what we know or our skills. People tend to misjudge what they know. We have a false sense of confidence. In my opinion is worrying living deceived and think what we don't need to learn more, to me it's like getting stuck in life. The point here is to wake up and be aware that you might no be smarter as you think.

Finally, the author gives us some tips to calibrate our judgments, we can test ourselves, do quizzes, etc. but what I can understand is that you have to take that knowledge and use it or prove it. One of the points that I take with me of this chapter is that only knowing a phrase or say it quickly doesn't mean that you know what it means instead we can create a mental model and try to explain it with our words or experiences.

Get beyond learning styles.

What I get most from this chapter is the fact that we sometimes believe that we have our learning style and this is not a valid theory still. We must understand that learning is not something linear, is not something that you just do and you do it the same way each time. No, I think that's what the author says, you aren't going to learn the same way new topics, learning is strange you have to be open to doing it in different ways. This is a theory that has no support.

The author also is inviting us to take different materials and approach this material in a different way depending on the material it needs to be learned differently. The other thing that points out from here is what you believe of yourself, this plays a big part in learning, so stay positive, you can do it.

Finally, the author gives us two important tools like dynamic testing which we can use to learn things by doing several steps, and in this way, we are working through the material and not the style. We can do an exam then see in which part I'm failing then practice and reflect on this part and finally test me again. What I like most is the building structures advice, and I'm going to apply it like this, first, get the main ideas out of a text, then try to recall everything and finally build something with my own words, try to explain what I just learned.

Increase your abilities and closing.

What I understood from this chapter is first, that we have a misconception about the brain, we think that we are born with a set intelligence and well, that's not the truth, brain and intelligence can change. Now, I think this is one of the points that people get stuck because they believe this.

The chapter shows to us many ways of how the brain can change, but I take with me some points. The first one is that is possible to change the brain, but once again it requires effort. Effortful learning changes the brain. The brain can heal itself, so why not help him and use it the way it should be. One important thing is that we need to change our way to think because we don't dare to things that we like and the more we do the more that we are going to learn. That's the growth mindset. Finally, remember to do retrieval learning and to practice and then use the cues. We can change our brains, remember!

Make it Stick.

This chapter is like a general summary of the book without going into details. It's kind of separated in three ways... Some strategies or topics we already review. I'm only going to focus on the student section and lifelong learners, il try to have a closing section where you could see the general tips, this may be repetitive but I want to close with this.

That's enough for the book.

Talking now with my own words, reading this book made me realize a lot of things. These are my learning, I want to start by saying that with this book I finally understood that learning is a process and it takes time and it's not linear. It's something hard to do and requires energy and time and we need to know this and stop being frustrated when we are trying to learn things. Now, I know that is hard but it's my responsibility to learn new things and try new things also to learn how to learn. From now on I'm going to be responsible for that part.

This book gave us some tips to have effective learning and what I can do now for learning is doing retrieval learning: Quiz myself, test myself, try to find the main ideas of a text, check my failures, and fix that. Interleave my study or practice sessions, leave a window of time between my study sessions, sometimes I could even interleave with 2 topics or more. Vary my practices or study sessions, practices the problem from different views, alternate between problems, mix the problem with other subjects or data.

I also liked the part to try to solve a problem without having a solution. I like to this a lot, take a material try to understand it, and then try to explain it. Doing these two things will take me to reflect and try to go deeper into what I'm learning. Now, what I won't ever do is reread, massed practice, think that I know everything and that learning stays with me.

In my opinion, this book can help anyone because gives you a different perception of what learning is and how we as human learn. I will recommend this book to anyone, I mean everyone is a lifelong learner. A really good book.

Code: The hidden language of computer hardware and software.

This book is trying to give us an explanation of how computers work, so ideas are different and I'll try to review differently because I'll try to take main ideas and try to make it like a story and pointing out what I can understand. Sorry, I know that the past book was so heavy in content but it was worth it, hope you like it.

This is how the story begins, we have a couple of friends and they are having problems communicating so they try different ways to do it with flashlights, at this point, we can create special patterns to communicate. At this point is where we get introduced to the meaning of code which we can understand the system for transferring information among people and machines.

Two things to point out here, in my opinion, codes were born because it was a need, the need of communicating. but take in mind that speaking is also a code. In this part, we can have as an example morse code.

Continuing with the morse code we can encounter two main issues, it seems like the code is easy to use but to send a message but you have to translate the written message to morse code and when you get the code you have to translate it back to normal letters. This what we know as encoding and decoding. One of the problems that a code can present is when you don't know where the character ends. Having only 2 characters a dot and a line make morse a binary code.

What we can understand now is that we have a code string that has only 2 characters, and in the book, we can find an example of braille, but the important thing here is to note that if the character flips it changes the meaning of the string. Depending on the number of characters that the code has it's the possible number of "words" that we can represent.

Now, this part is about the lamp but the things that must focus is on energy and components. We don't need a lot of details here but we need some things. A circuit is a circle and energy flows through a cable. We also know that energy flows in superconductors and the opposite is a resistor which is more resistant to the passage of energy and material that don't conduct energy are called insulators. Copper has very low resistance but has some. Voltage refers to the potential and current is related to the number of electrons in the circuit. Let's see some circuits.

This may seems crazy, but we need this. Now we have two new knowledges.

The main idea here and what the author (IMO), is trying to explain is that if we gather these two things we can send signals through wires with a certain pattern, like code morse. This how we can imagine the first system that could send signals through wires and could be interpreted, remember encode and decode.

This is how the telegraph works but it has the problem clicking the button. This can be solved with a simple device, which all computers use. It's a magnet, telegraph that I have seen a lot of them in movies, I never understood how that thing could work. I was flipping when I read that a telegraph is a wire connected to a magnet on both edges and what makes the "click" sound is the magnet receiving the electrical signals and attracting a metal and that was the sound, the crash of those two things. Then later on got fancier with a comfortable switch. Then later Morse notices that sound could be returned by putting a stick of wood I know it sounds strange but... it was a great idea, was called a repeater.

There is a big long story about numerals systems but we only need some things from this part, just FYI the number 10 is a special number for humans for many reasons but the most important is because is the number of fingers we have basically that's why we have number system based on 10 called decimal, we all know how it works. We can find alternatives to the decimal system, what I find interesting from here it's that states that if we had a different number of fingers 10 would mean another thing... I'm not very sure... We can also find the octal system. Anyway... We are here to discuss the binary system which only uses 1's and 0's to represent different numbers. New things here, binary numbers are big, this is the simpler way. Taking this in mind we can think of these systems as on/off, open/closed...

My opinion.

Sorry, I really wanted to take the main idea of the book and try to put it in my words, but there are a lot of things to say but will never finish. I do this. I wanted to keep the story saved for me later in someplace but that's not the point right now but I notice a lot of things by doing this and want to share it with you. That's why I prefer to talk about the book in general and why you should or not read it.

1. The plot and how it's told.

The story is going to start from code morse and how you could communicate with a neighbor, you are going to find lots of these examples throughout the book. The examples are very easy to understand and interesting and what I liked is that go straight to the point. You are also going to find the technical explanations, how the bit works, the boolean operators, how electrical circuits, how a lot of strange machines works, etc. If you are new to this is a great start but if you have some experience you could just reaffirm the knowledge. A lot of images and diagrams throughout the book. Until you get to know how a computer works.

The story is told more simply, I liked how the author takes hard concepts and puts them in other words. Also, you are not going to lose any detail, you can understand a chapter because of the previous one, the story is sequential so what you just read is going to be in the next chapter. I must admit that there were some chapters that I didn't get everything, I mean in some chapters has so much electrical circuits stuff.

2. The content

I was flipping with the content of the firsts chapters. I've been working with computers but only the software part and get to know all the circuit and electrical parts of hardware and other older machines was a whole discovery to me. I liked the content of those chapters because of that and they are really well told. The content, in general, is good, it's interesting, it's coherent like I said you don't get lost in the way. In a sentence, it's what you ever wanted to know about a computer and stop seeing like a magic box.

What causes me some trouble is the fact that it starts so slow and calmed and that's great, you can get ideas and try to understand them. But later in the book, it starts to rush a little bit in topics, it tries to explain how networks, multimedia, and graphical interfaces work and this is like too fast is not the same rhythm as the other topics. I think this could be part of another story, in my opinion, is kind of another area.

3. Reflection

My takeaways from this book, first of all, I learn and relearn some basic theoretical concepts and how they are applied to microprocessors. I must say that I didn't know how computers worked from the inside, I didn't know that such "basic" concepts were used to build a computer. This opens me to a new vision, this discovers the magic box that I use every day. Also, I have a notion of how older machines worked. I think this is the main learning of the book, and the book was so helpful to really understand it. With this book now I can tell the story (like I was trying to do... but I didn't finish).

My final reflection here is that in human history inventions come from need and need boost creativity and work. We didn't jump from anything to the computer, we can see in the book the historical context of inventors. The historical context can tell us a lot of things, but in this case what I can see is that a need is a cue for an invention, as I read in the book the telegraph was born from the need to communicate and so on. If the Motorola 6800 or Intel 8080 existed was for the need to automate calculations. What I can say from this is that needs changes and human inventions change with that, this is never going to end, nowadays we have different need and inventions are still changing. The story hasn't still finished we are part of it.

Learnings of the month

First I have to say that it was a hard month, we had a lot of tasks and a lot of things happening around but I have to say that I learned a lot. This my summary:

Innovation & hard/smart work

Communication skills are needed and we learn some tips on how to talk to anyone.
We also learned that ideas are worthless and what is important here is the execution.
We have an autopilot mechanism that is the response to a lot of things trying to get our attention and this mechanism makes important decisions about our life and time, we have to snap out of it.
It's a good idea to do a journal.
How 10,000 hours of practice doesn't make you a master.
How we can master anything.
How we can save cognitive resources.

Polyglot Programming

There are no geniuses programmers.
Working in a team is important.
Performance is part of an engineer's job.
Companies are made of people and they are important.
Act like an adult. Get your things done, your times, your work and no one is going to tell you what to do. They trust that you are an adult
It's not all about the technical stuff, is about social.
There is no best programming language.
Choose the language based on your situation.
Trying a new language sometimes takes time and effort, but you will have a new tool and maybe your problem solved.
Software is not written by tools, languages, frameworks, software is written by people.
Software tools and development environments have changed (improved) and bug rating is still constant.
Being productive is not multitasking or do more things.
Consciousness is not that smart.
You can learn new things, but you can't change intelligence.
Agile mindset is about going back and improve.
Agile mindset -> Give me the chance to learn.
Agile is about effort.

Fancy Topics

We sometimes need to live based on a principle.
Computers change quickly.
Machine Learning is a subfield of AI.
A Decision tree is easy to understand and visualize how they work.
Having a good dataset creates a good classifier.
To have a good dataset you should also have good features.
KNN algorithm
Neural Network
CART stands for Classification and Regression Trees.
Moonshot Thinking is when we choose a huge problem and propose to create a radical solution to the problem using disruptive technology.
Our presentations are important.
We should change the focus of the attention.
We should focus on the content.
Make people think.
Make people feel.
Quantum computers use qubits which are also two values (1 and 0) or both of them.
Superposition is like things being ghosting on each other. So, it can a 0 or a 1 or 1 and 0.
Quantum entanglement. Are still strongly related to each other, even though they can be a vast distance apart.
Quantum computers are used in IT security. Can find the secret key from the public key.
Quantum computers are also used in simulations.
Tunneling is the slippage between universes.
Quantum computers incorporate all of these theories in information processing.
They want quantum computers to solve optimization problems.
Qbism is about probabilities, the probability is about my belief.
Spontaneous collapse. Change the equation depending on the situation. It depends on the measurement.
If you have nothing to improve, you are going to pretend to have something to improve
I mean change starts within one, not on other things, you are going to change because you want to change, not because someone tells you.
UnitTest
Xunit
TDD
Maven
GIT
Vim

It’s all about science

We can find multiple devices to modify the bits, like capacitors or magnets.
That's how the information processing revolution works you have bits, you can have 0's and 1's and you flip them a lot of times.
Quantum computation is using data that the universe is computing. They are making the universe do things like perform quantum computations.
Quantum computation is once again the result of the universe's natural information processing revolution.
Quantum mechanics. It's about particles and waves.
You can build a quantum computer, you could take just a bunch of atoms in a molecule then zap them with microwaves. Each atom in this molecule listens to microwaves responds to microwaves of a slightly different frequency. We can flip the information like this. Just being careful you can make them compute.
Another way, you can set up a bunch of atoms trap them in an optical cavity, and zap them with lasers put a lot of circuits, and can do computation.
The Pretotyping Manifesto
Make sure you are building the right 'it' before building 'it' right.
With pretotyping, you're going to have very fast failure and because of that, you're going to have more time to try new things.
MVP: minimum viable product.
Pretotyping: Is to answer, would we use it?
Pretotyping is validating that you are building the right thing.
You create this prototype with the minimum resources you could waste. (money, energy, and time)
Quantum Machine Learning
The fact is that this feature that quantum mechanics is about vectors, matrices, linear operations
To construct quantum machine learning algorithms is to map ordinary machine learning algorithms when they have to do with manipulating vectors, figuring out inner products, performing linear transformations.
The point here is to transform the data so it can be computed in quantum.
The idea here is that quantum computers work because you're representing all these bits simultaneously.
Machine learning can be done better in the quantum state because you can find very interesting features.
In this part of bits and vectors we just need to understand that quantum mechanics allows manipulation of vectors in c^2^n using only n qubits.
Quantum random access, basically takes this information and codes in a quantum digital form.
To this point, we assume that we have the information in Qbits and the QRAM.
The quantum versions of these algorithms are exponentially faster than the classical ones.
Why you should have your own black box
A growth mindset recognizes that talent isn't enough and they have a lot of success stories.
The problem is when you're in the wrong culture, the intellectual and creative energy does not go towards learning but towards self-justification.
Richard Feynman
Testing & Automation

Well, forgive me for the extension and heaviness but thank you for reading what I learned during the month. I just want to share a book that I almost finish during the month. It's a story of cats, magic realism, Japanese things, and worlds.

Here is a look at why you should read this book. Why should you read “Kafka on the Shore”? - Iseult Gillespie

Thank you!

LFGA Web