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What are the top 3 methods used to find Autoregressive Parameters in Data Science?
In order to find autoregressive parameters, you will first need to understand what autoregression is. Autoregression is a statistical method used to create a model that describes data as a function of linear regression of lagged values of the dependent variable. In other words, it is a model that uses past values of a dependent variable in order to predict future values of the same dependent variable.
In time series analysis, autoregression is the use of previous values in a time series to predict future values. In other words, it is a form of regression where the dependent variable is forecasted using a linear combination of past values of the independent variable. The parameter values for the autoregression model are estimated using the method of least squares.
The autoregressive parameters are the coefficients in the autoregressive model. These coefficients can be estimated in a number of ways, including ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO). Once estimated, the autoregressive parameters can be used to predict future values of the dependent variable.
To find the autoregressive parameters, you need to use a method known as least squares regression. This method finds the parameters that minimize the sum of the squared residuals. The residual is simply the difference between the predicted value and the actual value. So, in essence, you are finding the parameters that best fit the data.
How to Estimate Autoregressive Parameters?
There are three main ways to estimate autoregressive parameters: ordinary least squares (OLS), maximum likelihood (ML), or least squares with L1 regularization (LASSO).
Ordinary Least Squares: Ordinary least squares is the simplest and most common method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values.
Maximum Likelihood: Maximum likelihood is another common method for estimating autoregressive parameters. This method estimates the parameters by maximizing the likelihood function. The likelihood function is a mathematical function that quantifies the probability of observing a given set of data given certain parameter values.
Least Squares with L1 Regularization: Least squares with L1 regularization is another method for estimating autoregressive parameters. This method estimates the parameters by minimizing the sum of squared errors between actual and predicted values while also penalizing models with many parameters. L1 regularization penalizes models by adding an extra term to the error function that is proportional to the sum of absolute values of the estimator coefficients.
Finding Autoregressive Parameters: The Math Behind It
To find the autoregressive parameters using least squares regression, you first need to set up your data in a certain way. You need to have your dependent variable in one column and your independent variables in other columns. For example, let’s say you want to use three years of data to predict next year’s sales (the dependent variable). Your data would look something like this:
| Year | Sales |
|——|——-|
| 2016 | 100 |
| 2017 | 150 |
| 2018 | 200 |
Next, you need to calculate the means for each column. For our sales example, that would look like this:
$$ \bar{Y} = \frac{100+150+200}{3} = 150$$
Now we can calculate each element in what’s called the variance-covariance matrix:
$$ \operatorname {Var} (X)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)^{2} $$
and
$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{n}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right) $$
For our sales example, that calculation would look like this:
$$ \operatorname {Var} (Y)=\sum _{i=1}^{3}\left({y_{i}}-{\bar {y}}\right)^{2}=(100-150)^{2}+(150-150)^{2}+(200-150)^{2})=2500 $$
and
$$ \operatorname {Cov} (X,Y)=\sum _{i=1}^{3}\left({x_{i}}-{\bar {x}}\right)\left({y_{i}}-{\bar {y}}\right)=(2016-2017)(100-150)+(2017-2017)(150-150)+(2018-2017)(200-150))=-500 $$
Now we can finally calculate our autoregressive parameters! We do that by solving this equation:
$$ \hat {\beta }=(X^{\prime }X)^{-1}X^{\prime }Y=\frac {1}{2500}\times 2500\times (-500)=0.20 $$\.20 . That’s it! Our autoregressive parameter is 0\.20 . Once we have that parameter, we can plug it into our autoregressive equation:
$$ Y_{t+1}=0\.20 Y_t+a_1+a_2+a_3footnote{where $a_1$, $a_2$, and $a_3$ are error terms assuming an AR(3)} .$$ And that’s how you solve for autoregressive parameters! Of course, in reality you would be working with much larger datasets, but the underlying principles are still the same. Once you have your autoregressive parameters, you can plug them into the equation and start making predictions!.
Which Method Should You Use?
The estimation method you should use depends on your particular situation and goals. If you are looking for simple and interpretable results, then Ordinary Least Squares may be the best method for you. If you are looking for more accurate predictions, then Maximum Likelihood or Least Squares with L1 Regularization may be better methods for you.
Autoregressive models STEP BY STEP:
1) Download data: The first step is to download some data. This can be done by finding a publicly available dataset or by using your own data if you have any. For this example, we will be using data from the United Nations Comtrade Database.
2) Choose your variables: Once you have your dataset, you will need to choose the variables you want to use in your autoregression model. In our case, we will be using the import and export values of goods between countries as our independent variables.
3) Estimate your model: After choosing your independent variables, you can estimate your autoregression model using the method of least squares. OLS estimation can be done in many statistical software packages such as R or STATA.
4) Interpret your results: Once you have estimated your model, it is important to interpret the results in order to understand what they mean. The coefficients represent the effect that each independent variable has on the dependent variable. In our case, the coefficients represent the effect that imports and exports have on trade balance. A positive coefficient indicates that an increase in the independent variable leads to an increase in the dependent variable while a negative coefficient indicates that an increase in the independent variable leads to a decrease in the dependent variable.
5)Make predictions: Finally, once you have interpreted your results, you can use your autoregression model to make predictions about future values of the dependent variable based on past values of the independent variables.
Conclusion: In this blog post, we have discussed what autoregression is and how to find autoregressive parameters.
Estimating an autoregression model is a relatively simple process that can be done in many statistical software packages such as R or STATA.
In statistics and machine learning, autoregression is a modeling technique used to describe the linear relationship between a dependent variable and one more independent variables. To find the autoregressive parameters, you can use a method known as least squares regression which minimizes the sum of squared residuals. This blog post also explains how to set up your data for calculating least squares regression as well as how to calculate Variance and Covariance before finally calculating your autoregressive parameters. After finding your parameters you can plug them into an autoregressive equation to start making predictions about future events!
We have also discussed three different methods for estimating those parameters: Ordinary Least Squares, Maximum Likelihood, and Least Squares with L1 Regularization. The appropriate estimation method depends on your particular goals and situation.
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Autoregressive Model
Autoregressive generative models can estimate complex continuous data distributions such as trajectory rollouts in an RL environment, image intensities, and audio. Traditional techniques discretize continuous data into various bins and approximate the continuous data distribution using categorical distributions over the bins. This approximation is parameter inefficient as it cannot express abrupt changes in density without using a significant number of additional bins. Adaptive Categorical Discretization (ADACAT) is proposed in this paper as a parameterization of 1-D conditionals that is expressive, parameter efficient, and multimodal. A vector of interval widths and masses is used to parameterize the distribution known as ADACAT. Figure 1 showcases the difference between the traditional uniform categorical discretization approach with the proposed ADACAT.
Each component of the ADACAT distribution has non-overlapping support, making it a specific subfamily of mixtures of uniform distributions. ADACAT generalizes uniformly discretized 1-D categorical distributions. The proposed architecture allows for variable bin widths and more closely approximates the modes of two Gaussians mixture than a uniformly discretized categorical, making it highly expressive than the latter. Additionally, a distribution’s support is discretized using quantile-based discretization, which bins data into groups with similar measured data points. ADACAT uses deep autoregressive frameworks to factorize the joint density into numerous 1-D conditional ADACAT distributions in problems with more than one dimension.
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- My interview for data scientistby /u/Ok_Squash1355 (Data Science) on September 26, 2023 at 12:30 pm
I have a interview for a data scientist intern in a product based company any advice about the topics i should be prepared better than any other as I'm not so good at ml. submitted by /u/Ok_Squash1355 [link] [comments]
- Should I add my money generating website to my CV?by /u/Z_Gunner (Data Science) on September 26, 2023 at 11:57 am
I created an analytics website that scraps data from different sources. My website shows some of this data and offers some analytics and visualizations. I barely get any revenue but it comes from ads placed on the site. I might start charging people for advanced analytics later but it's nothing solid at the moment. Would it be worth adding this site (+all the skills used) to my CV, or could potential employers see this as something that could distract me from work? Thanks in advance. submitted by /u/Z_Gunner [link] [comments]
- Passengers predictionby /u/ash_engyam (Data Science) on September 26, 2023 at 11:47 am
I have 5 columns of passengers count year to day, I received it daily but the problem is I don’t have Thursday and Friday data (MNAR), how to fill the cells with right numbers, what’s to do in this case by python or powerBI or excel ? submitted by /u/ash_engyam [link] [comments]
- Data Science as an undergrad, career development, personal projects and mentorship evaluationby /u/Topalope (Data Science) on September 26, 2023 at 11:12 am
Hello all, I am pursuing a bachelors in Data Science and am in a "Student Institute" program at my university that consists of five weeks of meetings, with the goal of helping students develop a better idea of who they are and what they want out of school. The program provides an opportunity to meet with a mentor within the field and the focus right now is on defining our individual visions. I'm reaching out to you all, because I am a returning student with experience as a senior bookkeeper and I was in the Army for six years working with primarily networking, but also radio equipment and electronics and optics repair. I am currently working as a CNC machinist in manufacturing for a top global company, and I want to leverage my experience to get me a better position as a project manager and head towards more executive leadership oriented roles, or perhaps get involved in more technical roles. I really enjoy talking to people, and I feel that I especially seem to have a knack for explaining concepts between parties who experience miscommunications due to bias or lack of broad realistic or industry specific experience. Usually, I can pretty quickly and easily identify the specific miscommunications. I am most certainly an early adopter when considering my place in new project rollouts, and I thoroughly enjoy providing feedback to anyone who cared to listen. I am in my early thirties and am more of a creative than a math wiz, but I very much enjoy tackling complex problems or challenges, especially when others claim them impossible or too daunting for any realistic approach to work. I am drawn to the challenges, and I think this may be in part reinforced from being a lifelong gamer. These days I am a father of two younglings, and so my challenges are more internal and involving my patience and planning than almost any other skill set. A lot of my motivation to join this group were due to the access to the mentor. I realized a few years ago that I mostly grow when working next to people who are using advanced skills which I then learn about by seeing them used in practice. Many of my bookkeeping skills came from watching the CPA make corrections, for example. I know that rubbing elbows with the most capable always leaves me feeling more confident and with a better idea of the domain and variables in which I operate. Learning new programs, hotkeys, formulae, concepts, and useful skills are all priorities for me, but ultimately I want to maximize my income while aligning my interests and skills. I understand that this isn't much information to go off of, but what kind of roles do YOU think fit my experience and goals? What roles do you think I should avoid? What would you focus on or aim towards with these kinds of skills? If you were building a team, where would you want someone like me? If you were to consider taking a mentee as a mentor, what might you see that I have written which would make you feel optimistic or pessimistic, or how might my statements drive your perceptions of me? My struggle has always been over disclosure, but I am certainly able to filter information once aware that the filter is beneficial. Thanks in advance!! submitted by /u/Topalope [link] [comments]
- Data Science or Health Data Scienceby /u/Noel_Jacob (Data Science) on September 26, 2023 at 10:46 am
I've got offer letter for studying in the UK for both of these. Which is better? The unis are almost of the same level, so which one is better career wise? submitted by /u/Noel_Jacob [link] [comments]
- Analysis of Back Pain Using Biomechanics and Artificial Intelligence (ML)by /u/SemperZero (Data Science) on September 26, 2023 at 10:26 am
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- Anyone complete Google Professional Machine Learning/AI Engineer Certification?by /u/Equal_Astronaut_5696 (Data Science) on September 26, 2023 at 10:25 am
Has completed this certification and found value in it? submitted by /u/Equal_Astronaut_5696 [link] [comments]
- Job Market Queryby /u/Quirky_Can_3470 (Data Science) on September 26, 2023 at 10:01 am
Has recession take a toll on the Data Science job market recently? Looking from the perspective of a fresh computer science graduate. submitted by /u/Quirky_Can_3470 [link] [comments]
- Please suggest imputation algorithm for a large datasetby /u/KRONOS-99 (Data Science) on September 26, 2023 at 9:26 am
I have a dataset having 9 lakh rows and 27 features. One of the columns has 3.77 lakh na values. It is a numerical column. I tried using knnimputer but I don't think it's ideal for data of this size. It kept running for several hours.I don't want to use mean, median, mode for imputation. Is there any other algorithm to impute numerical values? submitted by /u/KRONOS-99 [link] [comments]
- A Complete-ish Guide To Making Scientific Figures for Publication with Python and Matplotlibby /u/Estarabim (Data Science) on September 26, 2023 at 9:18 am
Not sure if this is the write place to post this, but as a research scientist I wrote a comprehensiveish guide to making figures in python for publication in scientific journals, thought it might be useful even for non-scientists who do data visualization. Edit: Link is here, sorry! https://dendwrite.substack.com/p/a-complete-ish-guide-to-making-scientific submitted by /u/Estarabim [link] [comments]
- [D] John Carmack and Rich Sutton partner to accelerate development of Artificial General Intelligenceby /u/blabboy (Machine Learning) on September 26, 2023 at 8:58 am
John Carmack, celebrated software engineer and founder of Keen Technologies, and Dr. Richard Sutton, Chief Scientific Advisor at the Alberta Machine Intelligence Institute (Amii) announce a partnership to bring greater focus and urgency to the creation of artificial general intelligence (AGI). This partnership is the first public milestone for Keen Technologies, following its initial funding announcement in August of 2022. The initial $20M round was led by Nat Friedman, Danial Gross, Patrick Collision, Tobi Lutke, Jim Keller, Sequoia Capital, and Capital Factory. In December 2022, Carmack departed as consulting CTO at Meta to focus his efforts on AGI. Sutton is the principal founder of the field of reinforcement learning. His work with Keen Technologies advances previously announced research priorities to understand basic computational intelligence. Through this work, documented in part in The Alberta Plan, Sutton seeks to understand and create long-lived computation agents that interact with a vastly more complex world and come to predict and control their sensory input signals. https://www.amii.ca/latest-from-amii/john-carmack-and-rich-sutton-agi/ submitted by /u/blabboy [link] [comments]
- Anyone with a speech impediment wants to practice mock interviews?by /u/lemeguesss (Data Science) on September 26, 2023 at 8:39 am
Hey! I have a stuttering which gets worse during interviews which is reducing my probability of getting into further rounds so if you want to practice make interviews. Please DM me. submitted by /u/lemeguesss [link] [comments]
- Master in Big Data / Data Scienceby /u/ContributionHungry73 (Data Science) on September 26, 2023 at 8:26 am
Es mi primer post, llevo unas semanas mirando master relacionados a Bid data o Dat science y tengo dudas, no se si alguien que hay realizado alguno, preferiblemente online, puede dejar un comentario con su experiencia y el titulo/universidad que fue… https://formacionpermanente.uned.es/tp_actividad/actividad/big-data-y-data-science-aplicados-a-la-economia-y-a-la-administracion-y-direccion-de-empresas Este es el que actualmente me llama mas la atención y si alguien lo ha hecho y puede comentarme qué tal le estaria muy agradecido, igual acepto sugerencias, pues vengo mi objetivo es comenzar en este mundo de Data, pero al partir de un grado en ADE es muy complicado encontrar curro con esta nueva vertiente… gracias🫡♥️ submitted by /u/ContributionHungry73 [link] [comments]
- What are some lesser known, but very useful data science tools in python?by /u/No_Boysenberry_7138 (Data Science) on September 26, 2023 at 8:23 am
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- [R] Vulnerability Management Dataset relatedby /u/confusedguy1395 (Machine Learning) on September 26, 2023 at 8:09 am
I am trying to make a project where I train the Vulnerability dataset(1. Vulnerability in code and 2. Generation of report) on Llama 2. for this i am trying to figure out appropriate dataset but cannot find one. Does anyone have some suggestion for me? I see that maybe NVD database could be helpful but i am not very much sure. submitted by /u/confusedguy1395 [link] [comments]
- [P] - VkFFT now supports quad precision (double-double) FFT computation on GPUby /u/xdtolm (Machine Learning) on September 26, 2023 at 8:03 am
Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. In the latest update, I have added support for quad-precision double-double emulation for FFT calculation on most modern GPUs. I understand that modern ML is going in the opposite low-precision direction, but I still think that it may be useful to have this functionality at least for some prototyping and development of concepts. The double-double approach to the evaluation of quads represents an FP128 number as an unevaluated sum of two double numbers (like 1 and 1e-17 - the second one is smaller than 1 ULP of the first one). This increases the significand from 53 to 106 bits, allowing to do operations on numbers varying up to 32 orders of magnitude. The range of numbers, however, stays the same as in double precision - 1.8 × 10^308 is the biggest representable number. The cost of this emulation is having approximately 6-10x more FP64 operations per kernel, so knowing that FP64 is bandwidth-bound on modern HPC GPUs, emulation of FP128 seems to be feasible. The platform of runtime code generation used in VkFFT allows to seamlessly make a full FFT implementation using the algorithmic base already present in VkFFT by just providing how double-double additions and multiplications work. So now double-double precision can be used to compute any FFT sequence you could do with VkFFT in double precision beforehand. Precision verification for powers of two (against quad precision FFTW), random input data from [-1;+1] range (sample 19): https://preview.redd.it/t71tg6nk1kqb1.png?width=3000&format=png&auto=webp&s=43d362d956d911478226b5670635ed6e15af3fab Benchmark results on AMD MI210 GPU, powers of two systems batched to 512MB FFT+iFFT. Bandwidth is calculated as 4 x system size (two uploads and two downloads from the chip) divided by the total execution time. Execution times are also provided near the results. Results are compared against the regular double precision (the same number of double precision systems, which takes 256MB). https://preview.redd.it/v8xu146l1kqb1.png?width=3000&format=png&auto=webp&s=5ff7adfa90b2dc006d9c42c3bda088272b8a2bc6 The results show that quad double-double precision is not bandwidth bound anymore and is 3-8x times slower due to the system taking 2x more memory and being 6-10x times more computationally expensive. The drop for big systems is related to them requiring more memory transfers and AMD GPUs having memory pin serialization issues for distant coalesced accesses. The presented version of the quad double-double code is not optimized fully, so performance improvements are expected in the near future. I will try to get Nvidia HPC GPU results as soon as I get access to them again. If you are interested in any aspect of VkFFT - I will be happy to answer questions! submitted by /u/xdtolm [link] [comments]
- [Discussion] Why should better networks be end-to-end? (Or why not?)by /u/piccadilly_nickadeli (Machine Learning) on September 26, 2023 at 7:21 am
TYPO IN THE TITLE: I wanted the title to be "why should neural networks be end-to-end?" My lazy ass was swipe texting on my phone and this typo happened. FML Especially in robotics, there's been a lot of research for end-to-end neutral networks where an image is the input and the control action is the output, for say, tasks like pick an object and place it somewhere. I feel this is very restrictive while developing such a solution because it tightly couples the "control" network to the "estimation" network. This reduces modularity in building the solution, i.e., if I find a better controller architecture (machine learning based or rule based) I'm unable to use that for the task I'm solving. Moreover it seems like the generalizability of this task suffers because training this network to do this task may make it very good at only doing that specific task and the explainability of the decision making goes out of the window because it is black box. Additionally doesn't more parameters mean requiring more data to train the model? I don't see much use from such network architectures. However, I do see the applications in machine translation where you simply train the encoder and decoder to the target language at the same time with a single loss function. It seems useful there, but maybe not so much in robotics. Can someone tell me their thoughts on end-to-end architectures? Let's have a healthy discussion Edit: typo submitted by /u/piccadilly_nickadeli [link] [comments]
- [D] Podcasts about AI and Machine Learning?by /u/darthJOYBOY (Machine Learning) on September 26, 2023 at 6:41 am
As the title says, what are the best podcasts to listen to that discuss new machine learning and AI advancements, new papers, and such? submitted by /u/darthJOYBOY [link] [comments]
- 🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍by /u/iamgeoknight (Data Science) on September 26, 2023 at 6:31 am
🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍 🌍 Spatial Analysis of Population Shifts: A Deep Dive into Raster-based Exploration 🌍 Dive into a comprehensive geospatial analysis of population shifts in Slovakia from 2006 to 2021. This tutorial showcases the power of raster data in identifying significant population changes over time. 📈 Key Takeaways: 🔍 Why rasterizing 1KM Grid Census Data is a game-changer. 🛠️ Step-by-step guide using Python libraries like geopandas, geocube, and xarray. 📌 Pinpointing areas with the most significant population shifts. 📊 Organizing, reprojecting, and saving results for further insights. submitted by /u/iamgeoknight [link] [comments]
- Power BI vs Tableauby /u/BusfahrerBernd999 (Data Science) on September 26, 2023 at 6:20 am
Hey everyone, so I have the opportunity to work more on data within my current job. I would be the only one in that type of field and would love to gain as much knowledge as I can to maybe later transition fully into a career in data. Should I choose to become an “expert” in Power BI or Tableau and why? Thank you for your advice submitted by /u/BusfahrerBernd999 [link] [comments]
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List of Freely available programming books - What is the single most influential book every Programmers should read
- Bjarne Stroustrup - The C++ Programming Language
- Brian W. Kernighan, Rob Pike - The Practice of Programming
- Donald Knuth - The Art of Computer Programming
- Ellen Ullman - Close to the Machine
- Ellis Horowitz - Fundamentals of Computer Algorithms
- Eric Raymond - The Art of Unix Programming
- Gerald M. Weinberg - The Psychology of Computer Programming
- James Gosling - The Java Programming Language
- Joel Spolsky - The Best Software Writing I
- Keith Curtis - After the Software Wars
- Richard M. Stallman - Free Software, Free Society
- Richard P. Gabriel - Patterns of Software
- Richard P. Gabriel - Innovation Happens Elsewhere
- Code Complete (2nd edition) by Steve McConnell
- The Pragmatic Programmer
- Structure and Interpretation of Computer Programs
- The C Programming Language by Kernighan and Ritchie
- Introduction to Algorithms by Cormen, Leiserson, Rivest & Stein
- Design Patterns by the Gang of Four
- Refactoring: Improving the Design of Existing Code
- The Mythical Man Month
- The Art of Computer Programming by Donald Knuth
- Compilers: Principles, Techniques and Tools by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman
- Gödel, Escher, Bach by Douglas Hofstadter
- Clean Code: A Handbook of Agile Software Craftsmanship by Robert C. Martin
- Effective C++
- More Effective C++
- CODE by Charles Petzold
- Programming Pearls by Jon Bentley
- Working Effectively with Legacy Code by Michael C. Feathers
- Peopleware by Demarco and Lister
- Coders at Work by Peter Seibel
- Surely You're Joking, Mr. Feynman!
- Effective Java 2nd edition
- Patterns of Enterprise Application Architecture by Martin Fowler
- The Little Schemer
- The Seasoned Schemer
- Why's (Poignant) Guide to Ruby
- The Inmates Are Running The Asylum: Why High Tech Products Drive Us Crazy and How to Restore the Sanity
- The Art of Unix Programming
- Test-Driven Development: By Example by Kent Beck
- Practices of an Agile Developer
- Don't Make Me Think
- Agile Software Development, Principles, Patterns, and Practices by Robert C. Martin
- Domain Driven Designs by Eric Evans
- The Design of Everyday Things by Donald Norman
- Modern C++ Design by Andrei Alexandrescu
- Best Software Writing I by Joel Spolsky
- The Practice of Programming by Kernighan and Pike
- Pragmatic Thinking and Learning: Refactor Your Wetware by Andy Hunt
- Software Estimation: Demystifying the Black Art by Steve McConnel
- The Passionate Programmer (My Job Went To India) by Chad Fowler
- Hackers: Heroes of the Computer Revolution
- Algorithms + Data Structures = Programs
- Writing Solid Code
- JavaScript - The Good Parts
- Getting Real by 37 Signals
- Foundations of Programming by Karl Seguin
- Computer Graphics: Principles and Practice in C (2nd Edition)
- Thinking in Java by Bruce Eckel
- The Elements of Computing Systems
- Refactoring to Patterns by Joshua Kerievsky
- Modern Operating Systems by Andrew S. Tanenbaum
- The Annotated Turing
- Things That Make Us Smart by Donald Norman
- The Timeless Way of Building by Christopher Alexander
- The Deadline: A Novel About Project Management by Tom DeMarco
- The C++ Programming Language (3rd edition) by Stroustrup
- Patterns of Enterprise Application Architecture
- Computer Systems - A Programmer's Perspective
- Agile Principles, Patterns, and Practices in C# by Robert C. Martin
- Growing Object-Oriented Software, Guided by Tests
- Framework Design Guidelines by Brad Abrams
- Object Thinking by Dr. David West
- Advanced Programming in the UNIX Environment by W. Richard Stevens
- Hackers and Painters: Big Ideas from the Computer Age
- The Soul of a New Machine by Tracy Kidder
- CLR via C# by Jeffrey Richter
- The Timeless Way of Building by Christopher Alexander
- Design Patterns in C# by Steve Metsker
- Alice in Wonderland by Lewis Carol
- Zen and the Art of Motorcycle Maintenance by Robert M. Pirsig
- About Face - The Essentials of Interaction Design
- Here Comes Everybody: The Power of Organizing Without Organizations by Clay Shirky
- The Tao of Programming
- Computational Beauty of Nature
- Writing Solid Code by Steve Maguire
- Philip and Alex's Guide to Web Publishing
- Object-Oriented Analysis and Design with Applications by Grady Booch
- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
- The Tao Te Ching
- The Productive Programmer
- The Art of Deception by Kevin Mitnick
- The Career Programmer: Guerilla Tactics for an Imperfect World by Christopher Duncan
- Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp
- Masters of Doom
- Pragmatic Unit Testing in C# with NUnit by Andy Hunt and Dave Thomas with Matt Hargett
- How To Solve It by George Polya
- The Alchemist by Paulo Coelho
- Smalltalk-80: The Language and its Implementation
- Writing Secure Code (2nd Edition) by Michael Howard
- Introduction to Functional Programming by Philip Wadler and Richard Bird
- No Bugs! by David Thielen
- Rework by Jason Freid and DHH
- JUnit in Action
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