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What are some ways to increase precision or recall in machine learning?
What are some ways to Boost Precision and Recall in Machine Learning?
Sensitivity vs Specificity?
In machine learning, recall is the ability of the model to find all relevant instances in the data while precision is the ability of the model to correctly identify only the relevant instances. A high recall means that most relevant results are returned while a high precision means that most of the returned results are relevant. Ideally, you want a model with both high recall and high precision but often there is a trade-off between the two. In this blog post, we will explore some ways to increase recall or precision in machine learning.

There are two main ways to increase recall:
by increasing the number of false positives or by decreasing the number of false negatives. To increase the number of false positives, you can lower your threshold for what constitutes a positive prediction. For example, if you are trying to predict whether or not an email is spam, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in more false positives (emails that are not actually spam being classified as spam) but will also increase recall (more actual spam emails being classified as spam).

To decrease the number of false negatives,
you can increase your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in fewer false negatives (actual spam emails not being classified as spam) but will also decrease recall (fewer actual spam emails being classified as spam).

There are two main ways to increase precision:
by increasing the number of true positives or by decreasing the number of true negatives. To increase the number of true positives, you can raise your threshold for what constitutes a positive prediction. For example, using the spam email prediction example again, you might raise the threshold for what constitutes spam so that fewer emails are classified as spam. This will result in more true positives (emails that are actually spam being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).
To decrease the number of true negatives,
you can lower your threshold for what constitutes a positive prediction. For example, going back to the spam email prediction example once more, you might lower the threshold for what constitutes spam so that more emails are classified as spam. This will result in fewer true negatives (emails that are not actually spam not being classified as spam) but will also decrease precision (more non-spam emails being classified as spam).

To summarize,
there are a few ways to increase precision or recall in machine learning. One way is to use a different evaluation metric. For example, if you are trying to maximize precision, you can use the F1 score, which is a combination of precision and recall. Another way to increase precision or recall is to adjust the threshold for classification. This can be done by changing the decision boundary or by using a different algorithm altogether.
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Sensitivity vs Specificity
In machine learning, sensitivity and specificity are two measures of the performance of a model. Sensitivity is the proportion of true positives that are correctly predicted by the model, while specificity is the proportion of true negatives that are correctly predicted by the model.
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Google Colab For Machine Learning
State of the Google Colab for ML (October 2022)

Google introduced computing units, which you can purchase just like any other cloud computing unit you can from AWS or Azure etc. With Pro you get 100, and with Pro+ you get 500 computing units. GPU, TPU and option of High-RAM effects how much computing unit you use hourly. If you don’t have any computing units, you can’t use “Premium” tier gpus (A100, V100) and even P100 is non-viable.
Google Colab Pro+ comes with Premium tier GPU option, meanwhile in Pro if you have computing units you can randomly connect to P100 or T4. After you use all of your computing units, you can buy more or you can use T4 GPU for the half or most of the time (there can be a lot of times in the day that you can’t even use a T4 or any kinds of GPU). In free tier, offered gpus are most of the time K80 and P4, which performs similar to a 750ti (entry level gpu from 2014) with more VRAM.
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For your consideration, T4 uses around 2, and A100 uses around 15 computing units hourly.
Based on the current knowledge, computing units costs for GPUs tend to fluctuate based on some unknown factor.
Considering those:
- For hobbyists and (under)graduate school duties, it will be better to use your own gpu if you have something with more than 4 gigs of VRAM and better than 750ti, or atleast purchase google pro to reach T4 even if you have no computing units remaining.
- For small research companies, and non-trivial research at universities, and probably for most of the people Colab now probably is not a good option.
- Colab Pro+ can be considered if you want Pro but you don’t sit in front of your computer, since it disconnects after 90 minutes of inactivity in your computer. But this can be overcomed with some scripts to some extend. So for most of the time Colab Pro+ is not a good option.
If you have anything more to say, please let me know so I can edit this post with them. Thanks!
Conclusion:
In machine learning, precision and recall trade off against each other; increasing one often decreases the other. There is no single silver bullet solution for increasing either precision or recall; it depends on your specific use case which one is more important and which methods will work best for boosting whichever metric you choose. In this blog post, we explored some methods for increasing either precision or recall; hopefully this gives you a starting point for improving your own models!
What are some ways we can use machine learning and artificial intelligence for algorithmic trading in the stock market?
Machine Learning and Data Science Breaking News 2022 – 2023
- Correct approach for Disaggregation of Forecastsby /u/mugiwaraMorrison (Data Science) on July 11, 2025 at 10:39 pm
Hey folks, I’m working on a disaggregation framework that breaks down store-level forecasts into register-level predictions using historical proportions. I'm trying to figure out the best practice for tuning this disaggregation logic. Specifically: When tuning the register-level proportions (identifying the best approach for eg. 4 week rolling avg vs 8 week rolling avg), should I multiply them with store-level actuals (to isolate disaggregation quality), or with store-level forecasts (to reflect production-like conditions)? My thinking is: During tuning, we should use actuals to get the cleanest signal on how good our proportion logic is, without injecting forecast noise. Then during validation, we apply those tuned proportions to store-level forecasts to simulate end-to-end accuracy. A colleague argues that since we care about register-level accuracy, we should use forecasts even during tuning. I feel this risks overfitting to forecast errors, which won’t repeat in future weeks. Would love to hear how others approach this — especially in time-series setups. Do you separate tuning and validation like this, or just evaluate everything end-to-end from the start? submitted by /u/mugiwaraMorrison [link] [comments]
- Am I in the wrong role or wrong company?by /u/thro0away12 (Data Science) on July 11, 2025 at 5:30 pm
I’ve been feeling really confused since the time I started my current job: I was hired as a data engineer and wanted to go into DE bc over the course of several years in data analytics, I realized that I enjoy writing code to find solutions to streamline data processes. Even if “coding” itself eventually becomes “taken over” by AI, I feel finding the right method to accomplish certain tasks will always be there. However, I feel like my job has left my morale low b/c of the lack of clarity and organization as to what I’m supposed to do. Here’s what I struggle with: There’s little/no documentation standard or practices at my job. Business requirements are all over the place saved in tickets, email convos, different excel sheets, in shared folders that have tons of random documents I must sift through to understand things. This is probably the biggest reason a task assigned for few hours takes me few days There’s not much appreciation to use code like Python or SQL to find ways to streamline/automate our tasks. The people who code more are other teams, we usually end up opening excel sheets and eyeballing in terms of data validation and quality. I would love to write code to streamline our processes. However writing reasonable code takes time, so instead I find a BS interim solution where I make tons of mistakes and end up looking dumb in return My team seems very interested in “business” and to me that’s fine except I don’t even know what that means. They want to spend time in us understanding business requirements and translating it. Problem is there’s no standard as to what that process entails. Usually, business requirements given to me are email conversations and I have to wait on another person in between me and business to coordinate and set up follow up meetings that takes forever I hoped that my current role would make me more technical except I feel less technical, overworked and burned out. I feel it’s very much a team thing but I fear going to another team or another role and having the same thing happen. I wish I could have been a backend engineer or different role instead. I still like a role where there’s some domain knowledge, but I’m not getting a chance to use that knowledge or any engineering skills either. It also feels my team has no interest in me becoming more technical bc they seem to think technical skills can eventually be taken over by AI or given to a more technical team or handed off to offshore contractors. I’m confused where my career is going esp in light of this new AI hype which is making it feel like programming jobs are over now. I’m lost. Help!! submitted by /u/thro0away12 [link] [comments]
- Doordash phone screen reject despite good in-interview feedback. What are they looking for?by /u/Substantial_Tank_129 (Data Science) on July 11, 2025 at 4:13 pm
Had a phone screen with DoorDash recently for a DS Analytics role. First round was a product case study — the interviewer was super nice, gave good feedback throughout, and even ended with “Great job on this round,” so I felt pretty good about it. Second round was SQL with 4 questions. Honestly, the first one threw me off — it was more convoluted than I expected, so I struggled a bit but managed to get through it. The 2nd and 3rd were much easier and I finished those without issues. The 4th was a bonus question where I had to explain a SQL query — took me a moment, but I eventually explained what it was doing. Got a rejection email the next day. I thought it went decently overall, so I’m a bit confused. Any thoughts on what might’ve gone wrong or what I could do better next time submitted by /u/Substantial_Tank_129 [link] [comments]
- Data science metaphors?by /u/idontknowotimdoing (Data Science) on July 9, 2025 at 7:51 pm
Hello everyone 🙂 Serious question: Does anyone have any data science related metaphors/similes/analogies that you use regularly at work? (I want to sound smart.) Thanks! submitted by /u/idontknowotimdoing [link] [comments]
- Quarterly to Monthly Data Conversionby /u/NervousVictory1792 (Data Science) on July 9, 2025 at 5:35 pm
As the title suggests. I am trying to convert average wage data, from quarterly to monthly. I need to perform forecasting on that. What is the best ways to do that?? . I don’t want to go for a naive method and just divide by 3 as I will loose any trends or patterns. I have come across something called disproportionate aggregation but having a tough time grasping it. submitted by /u/NervousVictory1792 [link] [comments]
- Reachy-Mini: Huggingface launched open-sourced robot that supports vision, text and speechby /u/Technical-Love-8479 (Data Science) on July 9, 2025 at 4:33 pm
Huggingface just released an open-sourced robot named Reachy-Mini, which supports all Huggingface open-sourced AI models, be it text or speech or vision and is quite cheap. Check more details here : https://youtu.be/i6uLnSeuFMo?si=Wb6TJNjM0dinkyy5 submitted by /u/Technical-Love-8479 [link] [comments]
- How do you guys measure AI impactby /u/Professional_Ball_58 (Data Science) on July 9, 2025 at 4:26 pm
Im sure a lot of companies are rolling out AI products to help their business. Im curious how do people typically try to measure these AI products impacts. I guess it really depends on the domain but can we isolate and see if any uplift in the KPI is attributable to AI? Is AB testing always to gold standard? Use Quasi experimental methods? submitted by /u/Professional_Ball_58 [link] [comments]
- All of my data comes from spreadsheets. As I receive more over time, what’s the best way to manage and access multiple files efficiently? Ideally in a way that scales and still lets me work interactively with the data?by /u/Proof_Wrap_2150 (Data Science) on July 9, 2025 at 4:07 pm
I’m working on a project where all incoming data is provided via spreadsheets (Excel/CSV). The number of files is growing, and I need to manage them in a structured way that allows for: Easy access to different uploads over time Avoiding duplication or version confusion Interactive analysis (e.g., via Jupyter notebooks or a lightweight dashboard) I’m currently loading files manually, but I want a better system. Whether that means a file management structure, metadata tagging, or loading/parsing automation. Eventually I’d like to scale this up to support analysis across many uploads or clients. What are good patterns, tools, or Python-based workflows to support this? submitted by /u/Proof_Wrap_2150 [link] [comments]
- Open source or not?by /u/SummerElectrical3642 (Data Science) on July 9, 2025 at 3:01 pm
Hi all, I am building an AI agent, similar to Github copilot / Cursor but very specialized on data science / ML. It is integrated in VSCode as an extension. Here is a few examples of use cases: - Combine different data sources, clean and preprocess for ML pipeline. - Refactor R&D notebooks into ready for production project: Docker, package, tests, documentation. We are approaching an MVP in the next few weeks and I am hesitating between 2 business models: 1- Closed source, similar to cursor, with fixed price subscription with limit by request. 2- Open source, pay per token. User can plug their own API or use our backend which offers all frontier models. Charge a topup % on top of token consumption (similar to Cline). The question is also whether the data science community would contribute to a vscode extension in React, Typescript. What do you think make senses as a data scientist / ML engineer? submitted by /u/SummerElectrical3642 [link] [comments]
- Saved $100k per year by explaining how AI/LLM work.by /u/tits_mcgee_92 (Data Science) on July 8, 2025 at 7:03 pm
I work in a data science field, and I bring this up because I think it's data science related. We have an internal website that is very bare bones. It's made to be simplistic, because it's the reference document for our end-users (1000 of them) use. Executives heard about a software that would be completely AI driven, build detailed statistical insights, and change the world as they know it. I had a demo with the company and they explained its RAG capabilities, but mentioned it doesn't really "learn" like the assumption AI does. Our repo is so small and not at all needed for AI. We have used a fuzzy search that has worked for the past three years. Additionally, I have already built out dashboards that retrieve all the information executives have asked for via API (who's viewing pages, what are they searching, etc.) I showed the c-suite executives our current dashboards in Tableau, and how the actual search works. I also explained what RAG is, and how AI/LLMs work at a high level. I explained to them that AI is a fantastic tool, but I'm not sure if we should be spending 100k a year on it. They also asked if I have built any predictive models. I don't think they quite understood what that was as well, because we don't have the amount of data or need to predict anything. Needless to say, they decided it was best not to move forward "for now". I am shocked, but also not, that executives want to change the structure of how my team and end-users digest information just because they heard "AI is awesome!" They had zero idea how anything works in our shop. Oh yeah, our company has already laid of 250 people this year due to "financial turbulence", and now they're wanting to spend 100k on this?! It just goes to show you how deep the AI train runs. Did I handle this correctly and can I put this on my resume? LOL submitted by /u/tits_mcgee_92 [link] [comments]
- Path to product managementby /u/FinalRide7181 (Data Science) on July 8, 2025 at 11:27 am
I’m a student interested in working as a product manager in tech. I know it’s tough to land a first role directly in PM, so I’m considering alternative paths that could lead there. My question is: how common is the transition from data scientist/product data scientist to product manager? Is it a viable path? Also would it make more sense to go down the software engineering route instead (even though I’m not particularly passionate about it) if it makes the transition to PM easier? submitted by /u/FinalRide7181 [link] [comments]
- How to deal with time series unbalanced situations?by /u/EducationalUse9983 (Data Science) on July 7, 2025 at 10:03 pm
Hi everyone, I’m working on a challenge to predict the probability of a product becoming unavailable the next day. The dataset contains one row per product per day, with a binary target (failure or not) and 10 additional features. There are over 1 million rows without failure, and only 100 with failure — so it's a highly imbalanced dataset. Here are some key points I’m considering: The target should reflect the next day, not the current one. For example, if product X has data from day 1 to day 10, each row should indicate whether a failure will happen on the following day. Day 10 is used only to label day 9 and is not used as input for prediction. The features are on different scales, so I’ll need to apply normalization or standardization depending on the model I choose (e.g., for Logistic Regression or KNN). There are no missing values, so I won’t need to worry about imputation. To avoid data leakage, I’ll split the data by product, making sure that each product's full time series appears entirely in either the training or test set — never both. For example, if product X has data from day 1 to day 9, those rows must all go to either train or test. Since the output should be a probability, I’m planning to use models like Logistic Regression, Random Forest, XGBoost, Naive Bayes, or KNN. Due to the strong class imbalance, my main evaluation metric will be ROC AUC, since it handles imbalanced datasets well. Would it make sense to include calendar-based features, like the day of the week, weekend indicators, or holidays? How useful would it be to add rolling window statistics (e.g., 3-day averages or standard deviations) to capture recent trends in the attributes? Any best practices for flagging anomalies, such as sudden spikes in certain attributes or values above a specific percentile (like the 90th)? My questions: Does this approach make sense? I’m not entirely confident about some of these steps, so I’d really appreciate feedback from more experienced data scientists! submitted by /u/EducationalUse9983 [link] [comments]
- Python package for pickup/advanced booking models for forecasting?by /u/GussieWussie (Data Science) on July 7, 2025 at 10:02 pm
Recently discovered pickup models that use reservation data to generate forecasts (see https://www.scitepress.org/papers/2016/56319/56319.pdf ) Seems used often in the hotel and airline industry. Is there a python package for this? Maybe it goes by a different name but I'm not seeing anything submitted by /u/GussieWussie [link] [comments]
- I don't drink, but I'm still tired because my dogs hate fireworks. Did everyone in the US take a long weekend at least?by /u/ElectrikMetriks (Data Science) on July 7, 2025 at 6:06 pm
submitted by /u/ElectrikMetriks [link] [comments]
- Weekly Entering & Transitioning - Thread 07 Jul, 2025 - 14 Jul, 2025by /u/AutoModerator (Data Science) on July 7, 2025 at 4:02 am
Welcome to this week's entering & transitioning thread! This thread is for any questions about getting started, studying, or transitioning into the data science field. Topics include: Learning resources (e.g. books, tutorials, videos) Traditional education (e.g. schools, degrees, electives) Alternative education (e.g. online courses, bootcamps) Job search questions (e.g. resumes, applying, career prospects) Elementary questions (e.g. where to start, what next) While you wait for answers from the community, check out the FAQ and Resources pages on our wiki. You can also search for answers in past weekly threads. submitted by /u/AutoModerator [link] [comments]
- With Generative AI looking so ominous, would there be any further research in any other domains like Computer Vision or NLP or Graph Analytics ever?by /u/Technical-Love-8479 (Data Science) on July 6, 2025 at 3:37 pm
So as the title suggest, last few years have been just Generative AI all over the place. Every new research is somehow focussed towards it. So does this mean other fields stands still ? Or eventually everything will merge into GenAI somehow? What's your thoughts submitted by /u/Technical-Love-8479 [link] [comments]
- Reliable DS Adjacent Fields Hiring for Bachelor's Degree?by /u/fenrirbatdorf (Data Science) on July 6, 2025 at 3:56 am
Hello all. To try and condense a lot of context for this question, I am an adult who went back to school to complete my bachelor's, in order to support myself and my partner on one income. Admittedly, I did this because I heard how good data science was as a field, but it seems I jumped in at the wrong time. Consequently, now that I am one year out from graduating with my bachelor's, I am starting to think about what fields would be best to apply in, beyond simply "data science" and "data analysis." Any leads on fields that are reliably hiring that are similar to data science but not exact? I am really open to anything that would pay the bills for two people. submitted by /u/fenrirbatdorf [link] [comments]
- What’s the best way to automate pulling content performance metrics from LinkedIn beyond just downloading spreadsheets?by /u/Proof_Wrap_2150 (Data Science) on July 5, 2025 at 9:01 pm
I’ve been stuck manually exporting post data from the LinkedIn analytics dashboard for months. Automating via API sounds ideal, but this is uncharted territory! submitted by /u/Proof_Wrap_2150 [link] [comments]
- Long-timers at companies — what’s your secret?by /u/mlbatman (Data Science) on July 5, 2025 at 8:35 pm
Hi everyone, I’ve been a job hopper throughout my career—never stayed at one place for more than 1-2 years, usually for various reasons. Now, I’m entering a phase where I want to get more settled. I’m about to start a new job and would love to hear from those who have successfully stayed long-term at a job. What’s the secret sauce besides just hard work and taking ownership? Lay your knowledge on me—your hacks, tips, rituals. Thanks in advance. submitted by /u/mlbatman [link] [comments]
- A Brief Guide to UVby /u/Daniel-Warfield (Data Science) on July 5, 2025 at 6:01 pm
Python has been largely devoid of easy to use environment and package management tooling, with various developers employing their own cocktail of pip, virtualenv, poetry, and conda to get the job done. However, it looks like uv is rapidly emerging to be a standard in the industry, and I'm super excited about it. In a nutshell uv is like npm for Python. It's also written in rust so it's crazy fast. As new ML approaches and frameworks have emerged around the greater ML space (A2A, MCP, etc) the cumbersome nature of Python environment management has transcended from an annoyance to a major hurdle. This seems to be the major reason uv has seen such meteoric adoption, especially in the ML/AI community. star history of uv vs poetry vs pip. Of course, github star history isn't necessarily emblematic of adoption. <ore importantly, uv is being used all over the shop in high-profile, cutting-edge repos that are governing the way modern software is evolving. Anthropic’s Python repo for MCP uses UV, Google’s Python repo for A2A uses UV, Open-WebUI seems to use UV, and that’s just to name a few. I wrote an article that goes over uv in greater depth, and includes some examples of uv in action, but I figured a brief pass would make a decent Reddit post. Why UV uv allows you to manage dependencies and environments with a single tool, allowing you to create isolated python environments for different projects. While there are a few existing tools in Python to do this, there's one critical feature which makes it groundbreaking: it's easy to use. Installing UV uv can be installed via curl curl -LsSf https://astral.sh/uv/install.sh | sh or via pip pipx install uv the docs have a more in-depth guide to install. Initializing a Project with UV Once you have uv installed, you can run uv init This initializes a uv project within your directory. You can think of this as an isolated python environment that's tied to your project. Adding Dependencies to your Project You can add dependencies to your project with uv add <dependency name> You can download all the dependencies you might install via pip: uv add pandas uv add scipy uv add numpy sklearn matplotlib And you can install from various other sources, including github repos, local wheel files, etc. Running Within an Environment if you have a python script within your environment, you can run it with uv run <file name> this will run the file with the dependencies and python version specified for this particular environment. This makes it super easy and convenient to bounce around between different projects. Also, if you clone a uv managed project, all dependencies will be installed and synchronized before the file is run. My Thoughts I didn't realize I've been waiting for this for a long time. I always found off the cuff quick implementation of Python locally to be a pain, and I think I've been using ephemeral environments like Colab as a crutch to get around this issue. I find local development of Python projects to be significantly more enjoyable with uv , and thus I'll likely be adopting it as my go to approach when developing in Python locally. submitted by /u/Daniel-Warfield [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
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- The Annotated Turing
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- 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
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- Growing Object-Oriented Software, Guided by Tests
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- Alice in Wonderland by Lewis Carol
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- About Face - The Essentials of Interaction Design
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- The Tao of Programming
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- Philip and Alex's Guide to Web Publishing
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- Effective Java by Joshua Bloch
- Computability by N. J. Cutland
- Masterminds of Programming
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- 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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Top 1000 Canada Quiz and trivia: CANADA CITIZENSHIP TEST- HISTORY - GEOGRAPHY - GOVERNMENT- CULTURE - PEOPLE - LANGUAGES - TRAVEL - WILDLIFE - HOCKEY - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.

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Health Health, a science-based community to discuss human health
- RFK’s new Head Start policy shows the U.S is losing its moral conscienceby /u/msnbc on July 11, 2025 at 8:15 pm
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- Arizona patient dies in emergency room from plagueby /u/nbcnews on July 11, 2025 at 4:40 pm
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- Tick bites are sending more people to the ER this summer. Experts explain why.by /u/CBSnews on July 11, 2025 at 4:29 pm
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- FDA to consider drug affordability when granting new vouchers to speed up approvals, Makary saysby /u/cnbc_official on July 11, 2025 at 3:52 pm
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- FDA issues highest risk warning for nationwide chocolate recallby /u/newsweek on July 11, 2025 at 10:26 am
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Today I Learned (TIL) You learn something new every day; what did you learn today? Submit interesting and specific facts about something that you just found out here.
- TIL England and Ireland had sumptuary laws preventing lower classes from eating, drinking and dressing like the typical elite class, after the economic boost to peasants post the black death plague.by /u/cuntdoc on July 12, 2025 at 5:10 am
submitted by /u/cuntdoc [link] [comments]
- TIL Sudden rodent die off can signal an incoming plague. Patient in AZ died today from bubonic plague. HHS in AZ are concerned about sudden prairie dogs deaths.by /u/GoinThruTheBigD on July 12, 2025 at 4:06 am
submitted by /u/GoinThruTheBigD [link] [comments]
- TIL of The Tale of Genji, one of the world's first novels, which was written by a Japanese noblewoman in the early 11th centuryby /u/bros402 on July 12, 2025 at 3:27 am
submitted by /u/bros402 [link] [comments]
- TIL that the world's oldest known prosthetic device is a 3,000-year-old wooden toe found on an Egyptian mummy. The "Cairo Toe" shows signs of wear, indicating it was actually used by its owner during their lifetime, not just attached for burial purposesby /u/CalmRipper on July 12, 2025 at 2:55 am
submitted by /u/CalmRipper [link] [comments]
- TIL that approximately 1 in every 5-6 million boys are born with two penisesby /u/DeScepter on July 12, 2025 at 2:31 am
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Reddit Science This community is a place to share and discuss new scientific research. Read about the latest advances in astronomy, biology, medicine, physics, social science, and more. Find and submit new publications and popular science coverage of current research.
- Microglia replacement halts the progression of microgliopathy in mice and humansby /u/acidgoat_15 on July 11, 2025 at 11:00 pm
submitted by /u/acidgoat_15 [link] [comments]
- Autistic adults overwhelmed by non-verbal social cues, describing the intense mental effort it takes to navigate nonverbal communication in a new study. These challenges often lead to misunderstandings from those around them. This mutual disconnect is known as the Double Empathy Problem.by /u/mvea on July 11, 2025 at 9:30 pm
submitted by /u/mvea [link] [comments]
- Lyme Disease study finds CDC test misses most cases of early Lymeby /u/John_Audience2765 on July 11, 2025 at 9:20 pm
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- In a national trial of 570 adults, suicidal ideation declined within 1 week of starting Opioid use disorder meds. Both buprenorphine-naloxone and extended-release naltrexone helped, but buprenorphine showed stronger early effects.by /u/Inquiring_minds42 on July 11, 2025 at 9:05 pm
submitted by /u/Inquiring_minds42 [link] [comments]
- Children (10-16Y) are at a similar risk of dehydration and hyperthermia in extreme heat as adults, contradicting previous advice that children are more susceptible to heat-related illnessesby /u/nohup_me on July 11, 2025 at 7:42 pm
submitted by /u/nohup_me [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- Femke Bol [NED] runs to meet record and a world leading time of 51.95 in the 400m hurdles at Monoco Diamond League meet.by /u/Oldtimer_2 on July 11, 2025 at 6:47 pm
submitted by /u/Oldtimer_2 [link] [comments]
- Wimbledon SF: Sinner beats Djokovic to reach final against Alcaraz (Sinner 6-3 6-3 6-4 Djokovic)by /u/kamikazzzzzze on July 11, 2025 at 5:54 pm
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- First Time Ever! Italy Qualify For ICC Men's T20 World Cup 2026by /u/Huge-Physics5491 on July 11, 2025 at 5:16 pm
submitted by /u/Huge-Physics5491 [link] [comments]
- Italy have qualified for T20 World Cup for the first time. They join the Netherlands in qualifying for next year's tournament from the European qualifier.by /u/DangerNoodle1993 on July 11, 2025 at 5:00 pm
submitted by /u/DangerNoodle1993 [link] [comments]
- Venus Williams, 45, accepts wild-card invitation for DC Openby /u/PrincessBananas85 on July 11, 2025 at 4:56 pm
submitted by /u/PrincessBananas85 [link] [comments]