Dependency injection

Enable a class to be generic by making it disown the responsibility of defining an object in itself. Make other classes define the input object for it.

If you want to change the input object , you dont need to change the original class with dependency injection.

dependency injection

A spring container contains a set of objects or beans.


Essence of Rich Dad Poor Dad

Identify what are assets and what are liabilities.

Increase asset column.

Don’t work for money, make money work for you.

Rich buy luxuries late.

Rich guys income statement and balance sheet.


How corporations help rich with taxes.


Corporations earn, spend and then pay taxes on the rest.

Individuals earn, get taxed and then spend.

Listing some assets:

  1. Businesses that do not require my presence.
  2. Stocks – Fortunes are made in new stock issues(new stocks are tax-free).
  3. Bonds.
  4. Income generating real estate.
  5. Notes (IOUs).
  6. Royalties from intellectual property.

Dimensions of financial literacy:

  1. Accounting:
  2. Investing:
  3. Understanding markets:
  4. The law:

How to read effectively

  1. Think of reading as an active pursuit rather than being passive.
  2. Don’t get bogged down in details, try to sprint through them and look for the gist of the author.
  3. Read with the intention of finding out the gist and pace yourself accordingly.
  4. Learn to adjust your speed based on the complexity of the content.
  5. Skim through the content if you know what he is going to say.
  6. Stop vocalization during reading.
  7. Don’t be a word by word reader, be a phrase reader.
  8. Try to speed up yourself in your reading, that will help you more concentrated on the content.
  9. Develop your vocabulary.
  10. Like any other skill, more you practice, better you become. Make sure you practice with the above principles.
  11. Finally, Don’t consider it as an ephemeral course but as a lifelong journey of betterment.

Value system and rules in AI

Let’s say we have a machine that understands all the high-level abstractions and patterns in data. Let’s say it has built on its own models to predict what’s coming next. Data science ends here presenting models and predictions to Entrepreneurs or executives to make a decision. But Is it intelligent at this stage? No, it has to be able to make decisions on its own to be truly intelligent.

So, what guides decision making process?

Value system.

A company makes its decision to maximize its profit. Profit is their value and that guides their decision-making process.

A man’s value of his survival deeply coded in his DNA motivates him to make proper decisions that could help him get proper food, shelter, clothes, sex etc.

Evolution has managed to deduce very rich and distinct emotions that could help us identify reinforcements in the real world. This makes it an imperative for any machine to be hard coded with these rules of value system to make use of reinforcement learning or other latest tech meme of that kind.

Whether it is chess or Go, if there is something it should definitely know before starting the game, it has to be the winning state and possibly evaluation functions for all different states possible in the Game. But that’s a small world to code valuations/reinforcements. How would you go about writing such codes for a machine to evaluate the humongous set of states, instances, objects in the real world? This makes me believe that modeling the value system or giving to a machine ability to build its own value system is at the core of AI. Too early to ignore Minsky’s rule-based systems.

Ideas to work on

Understanding customer personality from user data.


Objective: Learn the habits of the user from his digital activities. Reflect it on him for retro. predict his behavior next day.

what data?

Digital data: what apps and sites he checks with what frequency?

Time stats:

Financial stats:

Data about the lifetime of a product would be very valuable to recommend another product of that category at the right time.

Input modifier:

Suppose you have a function add with arguments a and b. We usually have certain assumptions or requirements about what format that input has to be in. In this case, it might be some numbers. what if they are strings? it will through an error. but of we give same input to a human, he would probably concatenate two strings or map each char to an integer and add the result or something of that sort.

So, input modifier is an imaginative agent which would manipulate input we have/given to fit into the function argument requirements. This would allow for more collaboration among different functionalities at our disposal and may open the door for creativity in computation. However, there would be a trade-off of little uncertainty in it.

When you give circle and square arguments to put together function, input modifier can think of it as one inside the other, one after the other and so many other possibilities. this uncertainty is inevitable even in human cognition.

Universal evaluation of each person’s impact on the world and fairly distributing wealth based on that.

Principle component analysis for text summarization.

App for updating customers with latest IPOs and their prospectus.

App for local retail – buying things based on their closeness for faster delivery.

Train a classifier to identify a student’s stage in Flow state graph in Udacity.

Model a situation as a recurrent neural network fed with a sequence where each sequence image is learned by a convolutional neural network.

Text summarization mandatory for NLP? when you read book, you constantly summarize and build a model in your brain and continue reading the book with that summary in context. So, summarizing text and feeding it back in an LSTM network can NLU more efficient.




Design and Analysis of algorithms

The most important principle to be an algorithm designer is not to be content.

Asymptotic analysis:




The master method for analyzing the running time of divide and conquer algorithms:

  1. Black box solution for running time of you input few parameters related to the recurrence.
  2. Assumptions: All recurrences are of equal size.
  3. Master method

n- original problem size

a- number of recurrences called in each instance, rate at which subproblems proliferate. (Evil)

b- The factor by which the input size is divided for calling recurrences.

d- Polynomial exponent of the remaining work needed to merge solutions for the final solution.

b^d – Rate of work shrinkage per subproblem. (good)

Case 1; Same work at each level.

Case 2: More work at the root level.

Case 3: More work at leaf level.

Algorithm Design paradigms:

  1. Divide and conquer:
    1. write the base case.
    2. Using recursion to solve subproblems of a problem.
    3. Combine subproblems
    4. Store the results of subproblems in a hashmap and use them to trim other repeating recursive paths. (Dynamic programming)
  2. Randomization: Like in quick sort.
    1. Decomposition principle:
  3. Greedy algorithms.
  4. Dynamic programming.



n-num of vertices, m-num of edges

Graph partitioning: Cuts of a graph, Minimum cut


  1. Adjacency matrix. O(n^2)
  2. Adjacency lists. O(n+m)

Strongly connected components: Regions where you can go from any node A to any node B in the directed graph.

Kosaraju’s 2-pass strongly connected components algorithm:

One of the smartest and beautiful algorithms.

The structure of internet:

Structure of internet.PNG

Further reading: Networks, crowds and markets.



arcs – Directed edges, ordered pair.