Learning neuroscience, machine learning, social science, and programming on your own

Christian 郑梵力 Ramsey

Christian 郑梵力 Ramsey

A collection from my self-study in neuroscience, machine learning and artificial intelligence, anthropology, psychology, design, biology, ecology, programming, mathematics, probability, language and philosophy

Image for post
A sampling of my book collection

As I’ve mentioned in previous posts, I’ve studied broadly and deeply over the last 10 years resulting in the completion of over 500+ nonfiction books, a seemingly endless amount of mostly free lectures and knowledge from repeated and deliberate practice. This has given rise in the past to the term autodidact, one who learns on their own, primarily outside of institutions.

autodidact — If you’re an autodidact you’ve done most of your learning on your own, outside of school. “Having learned Greek and Latin, as well as landscape painting and auto repair, without any formal training makes you quite the autodidact.” — Dictionary.com

I cannot say I identify with this label but I find it useful for this imagined audience. I am extremely grateful that when I decided to skip formal education that there were professors at MIT, Stanford, Harvard, Berkley, California State, and Khanacademy, as well as independent practitioners not represented by any institution who choose to upload their courses on Youtube or made them freely available online. I am not grateful because they are great schools (you should judge the content and teaching style rather than the badge someone’s title has) but because at the time they were the most accessible and many of the professor’s courses were excellent and it was all I knew. I am grateful for public libraries which allowed me to sit there for most of the day and just read and write without having to pay for anything to accept a $3.99 membership. I’m grateful for sites that transcribe or make copies of books to share with the public. I’m grateful for many things that made this possible such as free or incredible inexpensive courseware. came online (now called MOOC) was in its infancy (I started in 2009) and now more than ever there isn’t anything you want to learn that you can’t find online.

My self-created journey through acquiring knowledge stands on the shoulders of these gracious people and the contents of their books, courses, and talks. So I decided to put together a list of books, courses, and people that have changed my view of the world and fuelled my aspiration for deep knowledge and deep inquiry broken down by traditional subjects. This is not the exhaustive path I’ve taken but it is a curated version of it. Pick a subject or two and dive in. I advocate studying intensely using immersion techniques (such as my mindfulness-based concentration system called the huise de system.). This is not meant to be a guide of what you should read or watch or the path you should tread. Then why learn these subjects? You could pick totally different ones. Learning multiple subjects deeply will give you the ability to think across domains and understand the sheer complexity of living organisms and artificial systems. They will help you update your cognitive toolkit and if applied at the deepest level, you’ll avoid unwise chatter about how people “are” or reductive and wrong-headed views about how the world works and maybe, just maybe, you’ll contribute to something that is considerate of our deep situation. Lastly, by making yourself learn deeply and daily, you’ll be a part of a much-needed upgrade to societies common understanding of the world.

I will only include subjects I studied sufficiently deeply. So the bar is generally 3 textbooks and 4 books about it. This cuts the list down to neuroscience, machine learning and artificial intelligence, anthropology, psychology, design, biology, ecology, programming, mathematics and probability, language, and philosophy.

I never set out to learn all of the subjects together and I don’t recommend you take this as a recipe on what to study sequentially or exhaustively. Your studies should be inquiry-based (see my paper on knowledge systems without centralised institutions). You should study to figure out answers to any of the questions you have about how life works and your place in it. Pursuing knowledge can be inspired by seeing yourself or others struggling and wondering what to do or just being interested in what makes a clock tick. If you follow any question thoroughly enough, you’ll find yourself cutting across many so-called independent subjects (which are actually better defined as heaps that exist more in the sociological realm than they do in the realm of phenomenological reality ) because that’s the nature of reality; it is interdependent and any effect likely has a deeply multifactorial set of causes. So follow your nose but guide yourself with discipline all the way through.

Let us begin. For each subject, I’ll provide you with an overview, why study it, and a list of curated books, courses, and people with links that have inspired and informed me in that specific area of study. I’ve tried to pretend that every course, person, and the book didn’t cut across all the subjects to simplify but let’s keep in mind that everything is fuzzier than our language and concepts would have them appear.

read full article

Get in Touch

Related Articles

C, C++, C#, and Objective-C—What Are They

As you’ve poked around the world of tech terms trying to get a handle on all that industry jargon, you might have...

Learning neuroscience, machine learning, social science, and programming on your own

Christian 郑梵力 Ramsey A collection from my self-study in neuroscience, machine learning and artificial...

Helpful Infographics Visualize Complex Branches of Math and Science

t’s often easy to get stuck into a narrow view of what a particular field of study entails, but as Dominic Walliman reveals...

Get in Touch

20,829FansLike
2,399FollowersFollow
0SubscribersSubscribe

Latest Posts

C, C++, C#, and Objective-C—What Are They

As you’ve poked around the world of tech terms trying to get a handle on all that industry jargon, you might have...

Learning neuroscience, machine learning, social science, and programming on your own

Christian 郑梵力 Ramsey A collection from my self-study in neuroscience, machine learning and artificial...

Helpful Infographics Visualize Complex Branches of Math and Science

t’s often easy to get stuck into a narrow view of what a particular field of study entails, but as Dominic Walliman reveals...

Réseaux de neurones artificiels

Les réseaux de neurones, inspirés de la structure du cerveau humain, sont au cœur des progrès récents de l’intelligence artificielle. Dotés de capacités impressionnantes,...

Relation Mystérieuse entre Maths, Sciences et Nature

A. Benmohammed La Nature se révèle par des signes et des signaux que le physicien va alors filtrer pour mesurer des nombres. Ceci produit...