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What is the Best Machine Learning Algorithms for Imbalanced Datasets?
In machine learning, imbalanced datasets are those where one class heavily outnumbers the others. This can be due to the nature of the problem or simply because more data is available for one class than the others. Either way, imbalanced datasets can pose a challenge for machine learning algorithms. In this blog post, we’ll take a look at which machine learning algorithms are best suited for imbalanced datasets and why they tend to perform better than others.
For example, in a binary classification problem, if there are 100 observations, and only 10 of them are positive (the rest are negatives), then we say that the dataset is imbalanced. The ratio of positive to negative cases is 1:10.

There are a few reasons why some machine learning algorithms tend to perform better on imbalanced datasets than others. First, certain algorithms are designed to handle imbalanced datasets. Second, some algorithms are more robust to outliers, which can be more common in imbalanced datasets. And third, some algorithms are better able to learn from a limited amount of data, which can be an issue when one class is heavily outnumbered by the others.
Some of the best machine learning algorithms for imbalanced datasets include:
– Support Vector Machines (SVMs),
– Decision Trees,
– Random Forests,
– Naive Bayes Classifiers,
– k-Nearest Neighbors (kNN),
Of these, SVMs tend to be the most popular choice as they are specifically designed to handle imbalanced datasets. SVMs work by finding a hyperplane that maximizes the margin between the two classes. This helps to reduce overfitting and improve generalization. Decision trees and random forests are also popular choices as they are less sensitive to outliers than other algorithms such as linear regression. Naive Bayes classifiers are another good choice as they are able to learn from a limited amount of data. kNN is also a good choice as it is not sensitive to outliers and is able to learn from a limited amount of data. However, it can be computationally intensive for large datasets.
There are two main types of machine learning algorithms: supervised and unsupervised. Supervised algorithms tend to perform better on imbalanced datasets than unsupervised algorithms. In this blog post, we will discuss why this is so and look at some examples.
Supervised Algorithms
Supervised algorithms are those where the target variable is known. In other words, we have training data where the correct answers are already given. The algorithm then learns from this data and is able to generalize to new data. Some examples of supervised algorithms are regression and classification.
Unsupervised Algorithms
Unsupervised algorithms are those where the target variable is not known. With unsupervised algorithms, we only have input data, without any corresponding output labels. The algorithm has to learn from the data itself without any guidance. Some examples of unsupervised algorithms are clustering and dimensionality reduction.
Why Supervised Algorithms Perform Better on Imbalanced Datasets
The reason why supervised algorithms perform better on imbalanced datasets is because they can learn from the training data which cases are more important. With unsupervised algorithms, all data points are treated equally, regardless of whether they are in the minority or majority class.
For example, in a binary classification problem with an imbalanced dataset, let’s say that we want to predict whether a customer will default on their loan payment or not. We have a training dataset of 1000 customers, out of which only 100 (10%) have defaulted on their loan in the past.
If we use a supervised algorithm like logistic regression, the algorithm will learn from the training data that defaulting on a loan is rare (since only 10% of cases in the training data are Positive). This means that it will be more likely to predict correctly that a new customer will not default on their loan (since this is the majority class in the training data).
However, if we use an unsupervised algorithm like k-means clustering, all data points will be treated equally since there is no target variable to guide the algorithm. This means that it might incorrectly cluster together customers who have defaulted on their loans with those who haven’t since there is no guidance provided by a target variable.
Conclusion:
In conclusion, supervised machine learning algorithms tend to perform better on imbalanced datasets than unsupervised machine learning algorithms because they can learn from the training data which cases are more important.
Some machine learning algorithms tend to perform better on highly imbalanced datasets because they are designed to deal with imbalance or because they can learn from both classes simultaneously. If you are working with a highly imbalanced dataset, then you should consider using one of these algorithms.
Thanks for reading!
How are machine learning techniques being used to address unstructured data challenges?
Machine learning techniques are being used to address unstructured data challenges in a number of ways:
- Natural language processing (NLP): NLP algorithms can be used to extract meaningful information from unstructured text data, such as emails, documents, and social media posts. NLP algorithms can be trained to classify text data, identify key terms and concepts, and extract structured data from unstructured text.
- Image recognition: Machine learning algorithms can be used to analyze and classify images, enabling the automatic identification and classification of objects, people, and other elements in images. This can be useful for tasks such as image tagging and search, as well as for applications such as security and surveillance.
- Audio and speech recognition: Machine learning algorithms can be used to analyze and classify audio data, enabling the automatic transcription and translation of spoken language. This can be useful for tasks such as speech-to-text transcription, as well as for applications such as call center automation and language translation.
- Video analysis: Machine learning algorithms can be used to analyze and classify video data, enabling the automatic detection and classification of objects, people, and other elements in video. This can be useful for tasks such as video tagging and search, as well as for applications such as security and surveillance.
Overall, machine learning techniques are being used in a wide range of applications to extract meaningful information from unstructured data, and to enable the automatic classification and analysis of data in a variety of formats.
How is AI and machine learning impacting application development today?
Artificial intelligence (AI) and machine learning are having a significant impact on application development today in a number of ways:
- Enabling new capabilities: AI and machine learning algorithms can be used to enable applications to perform tasks that would be difficult or impossible for humans to do. For example, AI-powered applications can be used to analyze and classify large amounts of data, or to automate complex decision-making processes.
- Improving performance: AI and machine learning algorithms can be used to optimize the performance of applications, making them faster, more efficient, and more accurate. For example, machine learning algorithms can be used to improve the accuracy of predictive models, or to optimize the performance of search algorithms.
- Streamlining development: AI and machine learning algorithms can be used to automate various aspects of application development, such as testing, debugging, and deployment. This can help to streamline the development process and reduce the time and resources needed to build and maintain applications.
- Enhancing user experiences: AI and machine learning algorithms can be used to enhance the user experience of applications, by providing personalized recommendations, recommendations, or by enabling applications to anticipate and respond to the needs and preferences of users.
Overall, AI and machine learning are having a significant impact on application development today, and they are likely to continue to shape the way applications are built and used in the future.
How will advancements in artificial intelligence and machine learning shape the future of work and society?
Advancements in artificial intelligence (AI) and machine learning are likely to shape the future of work and society in a number of ways. Some potential impacts include:
- Automation: AI and machine learning algorithms can be used to automate tasks that are currently performed by humans, such as data entry, customer service, and manufacturing. This could lead to changes in the types of jobs that are available and the skills that are in demand, as well as to increased productivity and efficiency.
- Job displacement: While automation may create new job opportunities, it could also lead to job displacement, particularly for workers in industries that are more susceptible to automation. This could lead to social and economic challenges, including unemployment and income inequality.
- Increased efficiency: AI and machine learning algorithms can be used to optimize and streamline business processes, leading to increased efficiency and productivity. This could lead to economic growth and innovation, and could also help to reduce costs for businesses and consumers.
- Enhanced decision-making: AI and machine learning algorithms can be used to analyze large amounts of data and make more informed and accurate decisions. This could lead to improved outcomes in fields such as healthcare, finance, and education, and could also help to reduce bias and improve fairness.
Overall, the impact of AI and machine learning on the future of work and society is likely to be significant and complex, with both potential benefits and challenges. It will be important to consider and address these impacts as these technologies continue to advance and become more widely adopted.
- [D] The steps to do original research ( it's a rant as well )by /u/Snoo_65491 (Machine Learning) on February 16, 2025 at 11:14 am
I am a Master's Student in the UK. I have been reading papers on Diffusion for a while. I have contacted PhD students at my University and have expressed my interest in working with them. I thought that I would be helping them with their research direction. However, after talking to them, they told me to read some papers and then find a research idea. For Context, I am reading about Diffusion Models. The more I read, I realize that I lack some math fundamentals. I am filling those holes, through courses, books and articles. However, it takes time. I believe that this lack of fundamental understanding is stopping me from coming up with hypotheses. I can find some research gaps through recent survey papers, but I am not able to come up with any hypotheses or a solution. Am I heading in the right direction? Does understanding stuff from a fundamental standpoint help with producing novel research ideas? How to generate novel research ideas? If you have some tips, I would be glad to hear them. P.S. I have never published before. Therefore, I am sorry if I am missing something fundamental. submitted by /u/Snoo_65491 [link] [comments]
- [P] I built an open-source AI agent that edits videos fully autonomouslyby /u/Maximum_Instance_401 (Machine Learning) on February 16, 2025 at 11:09 am
submitted by /u/Maximum_Instance_401 [link] [comments]
- [D] torch.compile using hidet compilerby /u/Lime_Dragonfruit4244 (Machine Learning) on February 16, 2025 at 8:37 am
Has anyone tried using hidet as an altenative backend to torch inductor for torch.compile. https://pytorch.org/blog/introducing-hidet/ submitted by /u/Lime_Dragonfruit4244 [link] [comments]
- [P] Daily ArXiv filtering powered by LLM judge (with link to the project)by /u/MadEyeXZ (Machine Learning) on February 16, 2025 at 8:02 am
Link to the project: https://arxiv.ianhsiao.xyz Hey guys, in my previous reddit post: [P] Daily ArXiv filtering powered by LLM judge there wasn't an available link because I pasted the same comment on many subreddits so the system thought I was a spam and removed all of them (you can compare the displayed comment amount and the actual amount to verify). I'm sorry for that. That being said, I'm really interested to learn the communities' feedback so I'm posting this again. Thank you for your patience! submitted by /u/MadEyeXZ [link] [comments]
- [D] Self-Promotion Threadby /u/AutoModerator (Machine Learning) on February 16, 2025 at 3:15 am
Please post your personal projects, startups, product placements, collaboration needs, blogs etc. Please mention the payment and pricing requirements for products and services. Please do not post link shorteners, link aggregator websites , or auto-subscribe links. Any abuse of trust will lead to bans. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Meta: This is an experiment. If the community doesnt like this, we will cancel it. This is to encourage those in the community to promote their work by not spamming the main threads. submitted by /u/AutoModerator [link] [comments]
- [D] TorchRec or DGL for embedding trainingby /u/BigBayDragon (Machine Learning) on February 15, 2025 at 11:01 pm
Hi I'm looking for a library for training large scale of embeddings. Pytorch-Biggraph seemed no longer maintained. Now I'm deciding between TorchRec vs DGL. Which tool would you recommend and why? If neither, which library do you recommend? submitted by /u/BigBayDragon [link] [comments]
- Document Extraction [R]by /u/BloodedRose_2003 (Machine Learning) on February 15, 2025 at 9:07 pm
I am a new machine learning engineer, I am trying to solve a problem for couple of months, I need to extract key value pairs from invoices as requirement, I tried to solve it using different strategies and approaches none of them seems like working properly, I need to design a generic solution which will work on any invoices without dependent on invoice layouts. Moto---> To extract key value pairs like "provider details":["provider name", "provider address", "provider gst","provider pan"], recipient details":[same as provider], "po details":["date", total amount","description "] Issue I am facing when I am extracting the words using tesseract or pdfplumber the words are read left to right in some invoice formats the address and details of provider and recipient merging making the separation complex, Things I did so far--->Extraction using tesseract or pdfplumber, identifying GST DATE PAN using regex but for the address part I am still lagging I also read a blog https://medium.com/analytics-vidhya/invoice-information-extraction-using-ocr-and-deep-learning-b79464f54d69 Where he solved the same using different methodology, but I can't find those rcnn and masked rnn models Can someone explain this blog and help me to solve this ? I am a fresher so any help can be very helpful for me Thank you in advance! submitted by /u/BloodedRose_2003 [link] [comments]
- [D] Is my company missing out by avoiding deep learning?by /u/DatAndre (Machine Learning) on February 15, 2025 at 7:42 pm
Disclaimer: obviously it does not make sense to use a neural network if a linear regression is enough. I work at a company that strictly adheres to mathematical, explainable models. Their stance is that methods like Neural Networks or even Gradient Boosting Machines are too "black-box" and thus unreliable for decision-making. While I understand the importance of interpretability (especially in mission critical scenarios) I can't help but feel that this approach is overly restrictive. I see a lot of research and industry adoption of these methods, which makes me wonder: are they really just black boxes, or is this an outdated view? Surely, with so many people working in this field, there must be ways to gain insights into these models and make them more trustworthy. Am I also missing out on them, since I do not have work experience with such models? EDIT: Context is formula one! However, races are a thing and support tools another. I too would avoid such models in anything strictly related to a race, unless completely necessary. I just feels that there's a bias that is context-independent here. submitted by /u/DatAndre [link] [comments]
- Laptop with quadro rtx5000 is good for machine learning and Stable diffusion ? Allowed Tags: "[Discussion]", "[D]"by /u/Accomplished-Pass557 (Machine Learning) on February 15, 2025 at 5:42 pm
Laptop with quadro rtx5000 is good for machine learning and Stable diffusion ? my old laptop has been used for many years and want to buy a new one I found this deal Acer concept D7 Secondhand around 900-1,000 USD near my local area ( I'm worried about heat and maintenance. Because the ports on the board are reversed inside) If it's not stable, I can't work at all. And I have a budget for only one time. i think it's interesting deal because it still in good condition has vram up to 16 GB or should I go for a brand new Laptops with rtx4060 https://www.amazon.co.uk/Acer-ConceptD-CN715-71P-Creator-i7-9750H/dp/B08FX5SC2J submitted by /u/Accomplished-Pass557 [link] [comments]
- [D] MixUp and Manifold MixUpby /u/Significant-Joke5751 (Machine Learning) on February 15, 2025 at 5:16 pm
Hey everyone. How are your experiences with mixup and manifold mixup. I have eeg data which has due to intra and intersubjective variability a domain shift between train and val set. My intention was to smooth the decision boundaries of my model with it. But a result is training instability. I use a = 0.4 so I have only light interpolations. submitted by /u/Significant-Joke5751 [link] [comments]
- [D] Insane CPU utilization when using torch XLA to retrain GPT-2 small on a small datasetby /u/New-Skin-5064 (Machine Learning) on February 15, 2025 at 4:43 pm
I am trying to train GPT-2 on the works of William Shakespeare(7ish mb) and am using the Kaggle TPU v3-8 VM to do this. This is my training code: ```python layers = 12 emb_size = 768 n_heads = 12 dropout = 0.1 vocab_size = tokenizer.n_vocab ctx_size = 1024 batch_size = 8 steps = 10000 ... def train(index, tokenizer, layers, emb_size, n_heads, dropout, vocab_size, ctx_size, steps): device = xla.device() model = Transformer(layers, emb_size, n_heads, dropout, vocab_size, ctx_size).to(device) optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) for i in tqdm(range(steps)): model.train() with xla.step(): x, y = get_batch(data, batch_size) x = x.to(device) y = y.to(device) xm.master_print(f"X shape: {x[5]}") xm.master_print(f"Y shape: {y[5]}") out, loss = model(x, y) loss.backward() xm.optimizer_step(optimizer) optimizer.zero_grad() xm.master_print(loss.item()) if i % 10 == 0: x = tokenizer.encode("Hello, ") x = torch.tensor(x).to(device) xm.master_print(tokenizer.decode(list(model.generate(x, 1, 10)))) checkpoint = { 'model': raw_model.state_dict(), 'optimizer': optimizer.state_dict(), } torch.save(checkpoint, f"./ckpt-{i}.pt") ``` I put the train code in a python file and import it into the notebook to run using xla.launch. For some reason, the X and Y shapes are not printing when I run the code, and my CPU utilization shoots up crazy values. How do I fix this? https://preview.redd.it/e38l44jb0cje1.png?width=219&format=png&auto=webp&s=60dbacfc5dad4c2d33397a11907d56a9a65075b5 submitted by /u/New-Skin-5064 [link] [comments]
- [D] Have any LLM papers predicted a token in the middle rather than the next token?by /u/TheWittyScreenName (Machine Learning) on February 15, 2025 at 3:59 pm
I’m working on a project (unrelated to NLP) where we use essentially the same architecture and training as GPT-3, but we’re more interested in finding a series of tokens to connect a starting and ending “word” than the next “word”. Since we’re drawing a lot from LLMs in our setup, I’m wondering if there’s been any research into how models perform when the loss function isn’t based on the next token, but instead predicting a masked token somewhere in the input sequence. Eventually we would like to expand this (maybe through fine tuning) to predict a longer series of missing tokens than just one but this seems like a good place to start. I couldn’t find much about alternate unsupervised training schemes in the literature but it seems like someone must have tried this already. Any suggestions, or reasons that this is a bad idea? submitted by /u/TheWittyScreenName [link] [comments]
- [D] Time Series - Training Rolling Windows - How to Pick the Best Model?by /u/Ambitious-Pomelo-700 (Machine Learning) on February 15, 2025 at 3:26 pm
Hello, When you train your model on rolling windows times series, like in the below picture, what's your most common approach on picking the best model? https://preview.redd.it/cukx1ehdmbje1.png?width=522&format=png&auto=webp&s=1e1582941e2ed0d2c9853d3b2051392d3d5dcedb Let's say we are talking about linear models (type ARIMA), you'd get a set of coefficients on 'Pass 1', most likely a different set on 'Pass 2', etc. Which model are you picking in the end? Naturally, you want to think of the one with the best metric (whatever it is - let's say RMSE), but there is a bias in doing so imo. Imagine the best model is the one built on 'Pass 1' and you actual forecasting period is after 'Pass 5' - do you really want to pick the model built on the oldest data? Sure, it was the best then, but the one built on 'Pass 4' or 'Pass 5' may be better now. Do you see my point? Thank you submitted by /u/Ambitious-Pomelo-700 [link] [comments]
- [P] Daily ArXiv filtering powered by LLM judgeby /u/MadEyeXZ (Machine Learning) on February 15, 2025 at 11:14 am
submitted by /u/MadEyeXZ [link] [comments]
- [R] Evaluating Physical Concept Understanding in LLMs Through Abstract Grid-Based Tasksby /u/Successful-Western27 (Machine Learning) on February 15, 2025 at 7:21 am
This work introduces a structured assessment framework for evaluating physics understanding in LLMs, drawing from educational testing principles. The researchers developed a comprehensive test suite covering mechanics, thermodynamics, and electromagnetism using both quantitative and qualitative questions. Key technical aspects: - Multi-level assessment hierarchy ranging from fact recall to conceptual transfer - Controlled vocabulary to minimize linguistic pattern matching - Cross-context validation using parallel problems - Integration of numerical computation and conceptual explanation tasks - Standardized scoring rubrics based on educational assessment methods Main results: - GPT-4 achieved 76% accuracy on basic physics calculations - Performance dropped to 43% on cross-context transfer problems - Significant variance in performance across physics domains - Models showed strong correlation between mathematical ability and physics problem-solving - Systematic errors emerged when combining multiple physics concepts I think this methodology provides a more rigorous approach to understanding LLM capabilities than previous work. The educational testing framework helps distinguish between surface-level pattern matching and deeper conceptual understanding. This could lead to better benchmarks for measuring AI progress in scientific reasoning. I think the results highlight current limitations in LLMs' ability to transfer physics knowledge across contexts - something that's crucial for real scientific work. The systematic evaluation approach could be extended to other scientific domains. TLDR: New assessment framework based on educational testing principles reveals LLMs have decent physics calculation abilities but struggle with deeper conceptual understanding and knowledge transfer. Full summary is here. Paper here. submitted by /u/Successful-Western27 [link] [comments]
- [D] What's the most promising successor to the Transformer?by /u/jsonathan (Machine Learning) on February 15, 2025 at 6:17 am
All I know about is MAMBA, which looks promising from an efficiency perspective (inference is linear instead of quadratic), but AFAIK nobody's trained a big model yet. There's also xLSTM and Aaren. What do y'all think is the most promising alternative architecture to the transformer? submitted by /u/jsonathan [link] [comments]
- Unpaired modalities[D] [R]by /u/halfCursed (Machine Learning) on February 15, 2025 at 1:35 am
Hey guys! I am looking for a research topic that deals with multi-modal learning, but the modalities are not paired. To be more specific, in papers like CLIP, text-image pairs were present to train the model in a self-supervised manner. Similarly, FLAVA had both paired and unpaired text-image modalities datasets. Is there any research work that deals with learning from multiple unpaired, unlinked modalities? Any research paper or concept that you might have come across? submitted by /u/halfCursed [link] [comments]
- [P] DeepSeek on affordable home lab serverby /u/n3tcarlos (Machine Learning) on February 14, 2025 at 9:00 pm
Is it realistic to use an NVIDIA RTX 3060 12GB or RTX 4060 Ti 16GB for inference on some of the smaller DeepSeek models with Ollama on a home lab server? For example, can these setups handle summarizing large articles with RAG? I'm curious about how limiting the TPS speed and the 4K context window might be. submitted by /u/n3tcarlos [link] [comments]
- [P] GNNs for time series anomaly detectionby /u/Important-Gear-325 (Machine Learning) on February 14, 2025 at 5:56 pm
Hey everyone! 👋 For the past few months, my partner and I have been working on a project exploring the use of Graph Neural Networks (GNNs) for Time Series Anomaly Detection (TSAD). As we are near the completion of our work, I’d love to get feedback from this amazing community! 🔗 Repo: GraGOD - GNN-Based Anomaly Detection Any comments, suggestions, or discussions are more than welcome! If you find the repo interesting, dropping a ⭐ would mean a lot. : ) We're also planning to publish a detailed report with our findings and insights in the coming months, so stay tuned! The repo is still under development so don't be too harsh 🙂 Looking forward to hearing your thoughts! submitted by /u/Important-Gear-325 [link] [comments]
- Thesis choice - Algorithm fairness, explainable and trustworthy AI [D]by /u/ade17_in (Machine Learning) on February 14, 2025 at 4:32 pm
I know, it is not the perfect sub for this question, but I won't find experts elsewhere. I was recently offered a position with focus on algorithm fairness, XAI and label bias/choice uncertainty (UQ to be specific) and it is a long time commitment (PhD). The domain is medical imaging and this is what I always wanted to get into. Anyone working in similar domain or have experience with this subfield of AI? I see a lot of different packages and approaches and finding it hard getting started with it. Though joining is months away, I want to atleast get started. I also feel that this domain will be industry relevant and though it's niche, it will stay as long as we have AI systems running. Any opinions? Also anyone PhD/experts I can DM for a short chat? submitted by /u/ade17_in [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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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.

Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA

Health Health, a science-based community to discuss human health
- Measles continues to spread as Texas outbreak rises to 48 cases: Here's what to knowby /u/BothZookeepergame612 on February 16, 2025 at 3:51 am
submitted by /u/BothZookeepergame612 [link] [comments]
- Family whose 5-year-old was killed in a hyperbaric chamber is ‘absolutely devastated,’ attorney saysby /u/Forward-Answer-4407 on February 15, 2025 at 9:06 pm
submitted by /u/Forward-Answer-4407 [link] [comments]
- Revealed: Fake Online Pharmacies Thriving Despite DEA Crackdownby /u/newsweek on February 15, 2025 at 8:30 pm
submitted by /u/newsweek [link] [comments]
- After delay, CDC releases data signaling bird flu spread undetected in cows and peopleby /u/Yacht_Taxing_Unit on February 15, 2025 at 3:23 am
submitted by /u/Yacht_Taxing_Unit [link] [comments]
- In rural West Texas, a measles outbreak grows with no end in sightby /u/nbcnews on February 15, 2025 at 1:02 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 about Nicholas Longworth, who when another member of Congress touched his bald head and said, "Nice and smooth. Feels just like my wife's bottom." Longworth felt his own head and said: "Yes, so it does."by /u/ViceCatsFan on February 16, 2025 at 3:30 am
submitted by /u/ViceCatsFan [link] [comments]
- TIL In ancient times wearing socks was a symbol of wealth because only the rich could afford the material to make themby /u/PoodleBirds on February 16, 2025 at 2:53 am
submitted by /u/PoodleBirds [link] [comments]
- TIL about the Shitbox Rally, an Australian long distance motoring event that raises money for cancer research. Each car in the rally has to cost less than $950 USD and can't be AWD/4WD. The Rally has raised almost $30 million USD so far.by /u/CaravelClerihew on February 16, 2025 at 2:29 am
submitted by /u/CaravelClerihew [link] [comments]
- TIL there are cases in which women have eaten their own placentas after childbirth because they believed it helped with depression, post delivery bleeding, and improved mood; there is no evidence it does any of these things.by /u/ienjoylanguages on February 16, 2025 at 2:29 am
submitted by /u/ienjoylanguages [link] [comments]
- TIL that in 1992 CNN Headline News came seconds away from mistakenly announcing that President George HW Bush had died on a trip to Japanby /u/spmahn on February 16, 2025 at 2:27 am
submitted by /u/spmahn [link] [comments]
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.
- Marine mystery solved: How anemonefish avoid stings from their sea anemone hosts | Research finds sea anemones have evolved to maintain low levels of sialic acid in their skin mucus to avoid triggering the release of nematocysts (stinging cells) in their sea anemone hostsby /u/FunnyGamer97 on February 16, 2025 at 4:46 am
submitted by /u/FunnyGamer97 [link] [comments]
- Biohybrid hand actuated by multiple human muscle tissuesby /u/squishy_tech on February 16, 2025 at 1:16 am
submitted by /u/squishy_tech [link] [comments]
- Importance of the clitoris to women's sex lives has been underappreciated. Researchers discovered size of a woman's clitoris might be key to sexual function after pelvic surgery. Postoperative sexual function after surgery was associated with clitoral size, position, and shape.by /u/mvea on February 15, 2025 at 10:29 pm
submitted by /u/mvea [link] [comments]
- The U.S. Is Dustier — It’s Costing $154 Billion A Year. Research puts the economic impact of dust events on par with some of the most costly and destructive natural disasters, like hurricanes and other storms, and points to the importance of dust mitigation effortsby /u/Wagamaga on February 15, 2025 at 9:33 pm
submitted by /u/Wagamaga [link] [comments]
- Fluoxetine (SSRI) found to combat infections and sepsis in miceby /u/SupremeOwl48 on February 15, 2025 at 7:40 pm
submitted by /u/SupremeOwl48 [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- 4 Nations: Guentzel seals the Team USA win over Canada with an empty net goal. Sidney Crosby has lost his first international game since 2010by /u/SkepticalZebra on February 16, 2025 at 4:06 am
submitted by /u/SkepticalZebra [link] [comments]
- USA tops Canada in thriller to book spot in 4 Nations title game with 3-1 victoryby /u/Oldtimer_2 on February 16, 2025 at 4:05 am
submitted by /u/Oldtimer_2 [link] [comments]
- A split screen view of Mac McClung and Blake Griffin’s dunks over cars during the NBA Slam Dunk Contestby /u/nba on February 16, 2025 at 3:51 am
submitted by /u/nba [link] [comments]
- Defending NBA Dunk Contest champion Mac McClung (who is 6'2" tall) starts off the competition with thisby /u/Oldtimer_2 on February 16, 2025 at 3:21 am
submitted by /u/Oldtimer_2 [link] [comments]
- 4 Nations: Detroit's Dylan Larkin takes his shot and puts the USA up 2-1 vs Canadaby /u/SkepticalZebra on February 16, 2025 at 2:57 am
submitted by /u/SkepticalZebra [link] [comments]