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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:
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- 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.
- ML code generator [P]by /u/kamiurek (Machine Learning) on April 20, 2024 at 12:32 pm
submitted by /u/kamiurek [link] [comments]
- [R] mobilenetv2by /u/Eleonora467 (Machine Learning) on April 20, 2024 at 10:16 am
how can i change the input shape of mobileneyv2,(512,512,3) to (512,512,4) submitted by /u/Eleonora467 [link] [comments]
- [D] A slide which makes you feel oldby /u/xiikjuy (Machine Learning) on April 20, 2024 at 8:20 am
submitted by /u/xiikjuy [link] [comments]
- [R] Backpropagation through space, time, and the brainby /u/SeawaterFlows (Machine Learning) on April 20, 2024 at 3:02 am
Paper: https://arxiv.org/abs/2403.16933 Abstract: Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit assignment, remains, to a large extent, an open question. In Machine Learning, the answer is almost universally given by the error backpropagation algorithm, through both space (BP) and time (BPTT). However, BP(TT) is well-known to rely on biologically implausible assumptions, in particular with respect to spatiotemporal (non-)locality, while forward-propagation models such as real-time recurrent learning (RTRL) suffer from prohibitive memory constraints. We introduce Generalized Latent Equilibrium (GLE), a computational framework for fully local spatio-temporal credit assignment in physical, dynamical networks of neurons. We start by defining an energy based on neuron-local mismatches, from which we derive both neuronal dynamics via stationarity and parameter dynamics via gradient descent. The resulting dynamics can be interpreted as a real-time, biologically plausible approximation of BPTT in deep cortical networks with continuous-time neuronal dynamics and continuously active, local synaptic plasticity. In particular, GLE exploits the ability of biological neurons to phase-shift their output rate with respect to their membrane potential, which is essential in both directions of information propagation. For the forward computation, it enables the mapping of time-continuous inputs to neuronal space, performing an effective spatiotemporal convolution. For the backward computation, it permits the temporal inversion of feedback signals, which consequently approximate the adjoint states necessary for useful parameter updates. submitted by /u/SeawaterFlows [link] [comments]
- [N] Kaiming He's lecture on DL architecture for Representation Learningby /u/lkhphuc (Machine Learning) on April 20, 2024 at 12:57 am
https://youtu.be/D_jt-xO_RmI Extremely good lecture, highest signal to noise of historical architecture advances of DL. submitted by /u/lkhphuc [link] [comments]
- Do you think Reinforcement Learning still got it? [D]by /u/cyb0rg14_ (Machine Learning) on April 19, 2024 at 8:40 pm
Recently I've heard many people saying reinforcement learning itself hasn't shown any improvement in many years (maybe alphago was the last big thing). Whereas other field of AI has seen many SOTA architectures like 'Transformers' for Sequence based tasks and 'ResNet', 'Diffusers' & 'VAE' like architectures for Computer vision tasks. Thought I do believe, directly or indirectly, reinforcement learning still playing a crucial role behind LLMs like ChatGPT and Claude using 'RLHF' techniques. And in many other recent technologies including self driving cars and robots. I think this is just a cold winter going in this field, which will soon find a state of the art architecture in upcoming years (or this is what I hope) What's your thoughts? 🤔 submitted by /u/cyb0rg14_ [link] [comments]
- [P] TorchFix - a linter for PyTorch-using code with autofix supportby /u/kit1980 (Machine Learning) on April 19, 2024 at 6:13 pm
TorchFix is a Python code static analysis tool - a linter with autofix capabilities - for users of PyTorch. It can be used to find and fix issues like usage of deprecated PyTorch functions and non-public symbols, and to adopt PyTorch best practices in general: https://github.com/pytorch-labs/torchfix submitted by /u/kit1980 [link] [comments]
- [D] Is Google Set to Dominate the RAG Scene with Its Massive Data Resources?by /u/Few-Pomegranate4369 (Machine Learning) on April 19, 2024 at 5:06 pm
Hey everyone! It looks like in a few years, the basic large language models (LLMs) we use will get commoditised, and it won't really matter which one you pick. The next big thing could be LLMs that use Retrieval-Augmented Generation (RAG), which means they need a ton of data to work well. Given that Google has access to loads of data through its search engine, do you think they're in a better position to lead in this new phase compared to other companies? What do you all think? submitted by /u/Few-Pomegranate4369 [link] [comments]
- [P] AI-based Language Teacher that can run locally on a 12GB graphics card (RTX 4070)by /u/HichamEB (Machine Learning) on April 19, 2024 at 2:43 pm
I've been playing around with various open-source models lately. One fun application that I figured I could try was a <Language Teacher> 🌍 The result are not half bad, you can give it a try here: https://github.com/helboukkouri/virtual-teacher submitted by /u/HichamEB [link] [comments]
- [P] End-to-end locally-running language teacherby /u/HichamEB (Machine Learning) on April 19, 2024 at 1:44 pm
Hey! Given how easy it is to just grab an open-source model and run it locally these days, I figured I'd try to make some kind of Language Teacher with whom I'd casually have discussions and learn new phrases on the go. This is a quick test in English/Spanish: https://www.loom.com/share/f0dbed21254f445b9d5b0a8e11270982 I used an LLM for the underlying chatbot, a TTS model for speaking out the answers and an ASR model for transforming my speech into an input for the LLM. Let me know if you have any comments 🙂 submitted by /u/HichamEB [link] [comments]
- [D] Embeddings search "drowning" in a sea of noise! Can you solve this riddle?by /u/grudev (Machine Learning) on April 19, 2024 at 1:21 pm
I'm writing a proof of concept for a RAG application for hundreds of thousands of textual records stored in a Postgres DB, using pgvector to store embeddings ( and using an HNSW index). Vector dimensions are specified correctly. Currently running experiments using varied chunk sizes for the text and comparing two different embedding models. (actual chunk size can vary a little because I am not breaking words to force a size). nomic-embed-text snowflake-arctic-embed-m-long Here's the gist experiment: 1- Create embeddings for "n" documents 2- Create a list of queries/prompts for information that is assuredly contained in SOME of those documents. Examples: What were the events that happened at "location x"? What is the John Doe's nickname? Who were the patients that checked into "hospital name"? Tell me about a requisition made by the director of sales. ... 3- For each query/prompt, I run a cosine distance query and get the the nearest 5 matching chunks. 4- After calculating the average distance for all queries/chunks, the lowest value is, in theory, the best combination of model/chunk_size. This worked SUPER well with a small sample of documents (say ≃ 200), but once I added more documents I started noticing an issue. Some of the NEW documents contain lists of literally 30k+ names. Whenever I ran a query that contains names, chunks from the lists above are returned, EVEN IF THEY DON'T CONTAIN THE NAMES, or any of the other information presented in the prompt (this happens regardless of the chose chunk size or strategy). My theory is that when a chunk containing names is embedded, the resulting embedding contain a strong vector for the semantic meaning of "name", but the vectors that differentiate that name from others can be relatively weak. A chunk containing almost nothing but references to the vector for "name" is then considered very similar to the prompt's embeddings, despite the names themselves not matching. For those of you with more experience/understanding, am I wrong in these assumptions? Would you have any suggestions/workarounds? I have some ideas but would like to see if anyone faced the same issues. submitted by /u/grudev [link] [comments]
- [R] The roles of value, key, and query in the diffusion model.by /u/Candid_Finish444 (Machine Learning) on April 19, 2024 at 1:10 pm
I am trying to replace the key, query, and value in different prompts of the diffusion model for video editing. I want to understand why key, query, and value are effective and what they represent in the diffusion model. https://preview.redd.it/uoce1dh4rfvc1.png?width=1086&format=png&auto=webp&s=24d6504ca9c50d9f5924dd935204db6c15484a16 submitted by /u/Candid_Finish444 [link] [comments]
- [P] How to obtain the mean and std from the rms to obtain the first prediction time for a time series case study ?by /u/Papytho (Machine Learning) on April 19, 2024 at 8:59 am
Hello I am trying to implement this from a paper: First, select the first l sampling points in the sampling points of bearing faults and calculate the mean μ_rms and standard deviation σ_rms of their root mean square values, and establish a 3σ criterion- based judgment interval [μ_rms − 3σ_rms, μ_rms +3σ_rms] accordingly. 2) Second, calculate the RMS index for the l + 1 th point FPTl+1 and compare it with the decision interval in step 1. If its value is not in this range, then recalculate the judgment interval after making l =l + 1. If its value is within this range, a judgment is triggered once. 3) Finally, in order to avoid false triggers, three consecutive triggers are used as the identification basis for the final FPT, and make this time FPTl = FPT The paper title: Physics guided neural network: Remaining useful life prediction of rolling bearings using long short-term memory network through dynamic weighting of degradation process My question is: how do I get the μ_rms and σ_rms from the RMS? What I did in this case was first sample the data and then calculate the RMS on the samples. But then I recreate sequences from these RMS values (which doesn't seem logical to me) and then calculate the μ_rms and σ_rms. I do use this value I obtain to do the interval and compare it with the RMS value. But the problem is that by doing this, it triggers way too early. This is the code I have made: def find_fpt(rms_sample, sample): fpt_index = 0 trigger = 0 for i in range(len(rms_sample)): upper = np.mean(rms_sample[i] + 3 * np.std(rms_sample[i])) lower = np.mean(rms_sample[i] - 3 * np.std(rms_sample[i])) rms = np.mean(np.square(sample[i + 1]) ** 2) if upper > rms > lower: if trigger == 3: fpt_index = i break trigger += 1 else: trigger = 0 print(trigger) return fpt_index def sliding_window(data, window_size): return np.lib.stride_tricks.sliding_window_view(data, window_size) window_size = 20 list_bearing, list_rul = load_dataset_and_rul() sampling = sliding_window(list_bearing[0][::100], window_size) rms_values = np.sqrt(np.mean(np.square(sampling) ** 2, axis=1)) rms_sample = sliding_window(rms_values, window_size) fpt = find_fpt(rms_sample,sampling) submitted by /u/Papytho [link] [comments]
- Any ways to improve TabNet..??? [D]by /u/Shoddy_Battle_5397 (Machine Learning) on April 19, 2024 at 8:06 am
so i was experimenting with tabnet architecture by google https://arxiv.org/pdf/1908.07442.pdf and found that if the data has a lot of randomness and noice then only it can outperform based on my dataset, but traditional machine learning algo like xgboost, random forest do a better job at those dataset where the features are robust enough but they fail the zero shot test and the transformer show some accuracy in that, so i just wanted to check if its possible to merge both of the traditional techniques and the transformer architecture so that it can perform better at traditional ml algo datasets and also give a good zero shot accuracy. while trying to merge it i found that in the tabnet paper they assume that each feature is independent and do not provide any place for any relationship with the features itself but the Tabtransformer architecture takes it into account https://arxiv.org/pdf/2012.06678.pdf as well but doesnt have any feature selection as proposed in tabnet.... i tried to merge them but was stuck where i have to do feature selection on the basis of the dimension assigned to each feature, while this work i s done by sparsemax in the tabnet paper i cant find a way to do that... any help would be appreciated submitted by /u/Shoddy_Battle_5397 [link] [comments]
- [R] Machine learning from 3D meshes and physical fieldsby /u/SatieGonzales (Machine Learning) on April 19, 2024 at 7:38 am
Ansys has released an AutoML product for physical simulation called Ansys Sim AI (https://www.ansys.com/fr-fr/news-center/press-releases/1-9-24-ansys-launches-simai). As a machine learning engineer, I wonder what types of models can be used to train on 3D mesh data in STL format with physical fields. How can the varying dimensions of input and output data be managed for different geometric objects? Does anyone have any ideas on this topic? submitted by /u/SatieGonzales [link] [comments]
- [Discussion] Are there specific technical/scientific breakthroughs that have allowed the significant jump in maximum context length across multiple large language models recently?by /u/analyticalmonk (Machine Learning) on April 19, 2024 at 6:28 am
Latest releases of models such as GPT-4 and Claude have a significant jump in the maximum context length (4/8k -> 128k+). The progress in terms of number of tokens that can be processed by these models sound remarkable in % terms. What has led to this? Is this something that's happened purely because of increased compute becoming available during training? Are there algorithmic advances that have led to this? submitted by /u/analyticalmonk [link] [comments]
- Probability for Machine Learning [D]by /u/AffectionateCoyote86 (Machine Learning) on April 19, 2024 at 4:47 am
I'm a recent engineering graduate who's switching roles from traditional software engineering ones to ML/AI focused ones. I've gone through an introductory probability course in my undergrad, but the recent developments such as diffusion models, or even some relatively older ones like VAEs or GANs require an advanced understanding of probability theory. I'm finding the math/concepts related to probability hard to follow when I read up on these models. Any suggestions on how to bridge the knowledge gap? submitted by /u/AffectionateCoyote86 [link] [comments]
- [D] How to evaluate RAG - both retrieval and generation, when all I have is a set of PDF documents?by /u/awinml1 (Machine Learning) on April 19, 2024 at 4:43 am
Say I have 1000 PDF docs that I use as input to a RAG Pipeline. I want to to evaluate different steps of the RAG pipeline so that I can measure: - Which embedding models work better for my data? - Which rerankers work and are they required? - Which LLMs give the most factual and coherent answers? How do I evaluate these steps of the pipeline? Based on my research, I found that most frameworks require labels for both retrieval and generation evaluation. How do I go about creating this data using a LLM? Are there any other techniques? Some things I found: For retrieval: Use a LLM to generate synthetic ranked labels for retrieval. Which LLM should I use? What best practices should I follow? Any code that I can look at for this? For Generated Text: - Generate Synthetic labels like the above for each generation. - Use a LLM as a judge to Rate each generation based on the context it got and the question asked. Which LLMs would you recommend? What techniques worked for you guys? submitted by /u/awinml1 [link] [comments]
- [Project] RL projectby /u/Valuable-Wishbone276 (Machine Learning) on April 19, 2024 at 4:36 am
Hi everyone. I want to build this idea of mine for a class project, and I wanted some input from others. I want to build an AI algorithm that can play the game Drift Hunters (https://drift-hunters.co/drift-hunters-games). I imagine I have to build some reinforcement learning program, though I'm not sure exactly how to organize state representations and input data. I also imagine that I'd need my screen to be recorded for a continuous period of time to collect data. I chose this game since it's got three very basic commands(turn left, turn right, and drive forward) and the purpose of the game(which never ends) is to maximize drift score. Any ideas are much appreciated. lmk if u still need more info. Thanks everyone. submitted by /u/Valuable-Wishbone276 [link] [comments]
- [R] Unifying Bias and Unfairness in Information Retrieval: A Survey of Challenges and Opportunities with Large Language Modelsby /u/KID_2_2 (Machine Learning) on April 19, 2024 at 4:34 am
PDF: https://arxiv.org/abs/2404.11457 GitHub: https://github.com/KID-22/LLM-IR-Bias-Fairness-Survey Abstract: With the rapid advancement of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift. This evolution, while heralding new opportunities, introduces emerging challenges, particularly in terms of biases and unfairness, which may threaten the information ecosystem. In this paper, we present a comprehensive survey of existing works on emerging and pressing bias and unfairness issues in IR systems when the integration of LLMs. We first unify bias and unfairness issues as distribution mismatch problems, providing a groundwork for categorizing various mitigation strategies through distribution alignment. Subsequently, we systematically delve into the specific bias and unfairness issues arising from three critical stages of LLMs integration into IR systems: data collection, model development, and result evaluation. In doing so, we meticulously review and analyze recent literature, focusing on the definitions, characteristics, and corresponding mitigation strategies associated with these issues. Finally, we identify and highlight some open problems and challenges for future work, aiming to inspire researchers and stakeholders in the IR field and beyond to better understand and mitigate bias and unfairness issues of IR in this LLM era. https://preview.redd.it/3glvv92v6dvc1.png?width=2331&format=png&auto=webp&s=af66f2bf082620882f09ea744eda88cf06c67112 https://preview.redd.it/d48pt3sw6dvc1.png?width=1126&format=png&auto=webp&s=2343460399473bde3f5e37c0bbcfdc88ffc81efb submitted by /u/KID_2_2 [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 health news and the coronavirus (COVID-19) pandemic
- Doctors in Somalia discover a patient with four kidneysby /u/Impossible-Search94 on April 20, 2024 at 2:28 am
submitted by /u/Impossible-Search94 [link] [comments]
- U.S. measles cases reach 125 this year, topping 2022's large outbreaksby /u/CBSnews on April 19, 2024 at 7:49 pm
submitted by /u/CBSnews [link] [comments]
- H5N1 Strain Of Bird Flu Found In Milk: WHOby /u/maztabaetz on April 19, 2024 at 6:50 pm
submitted by /u/maztabaetz [link] [comments]
- Toxic chemicals in everyday products "enter the human body" via touchby /u/newsweek on April 19, 2024 at 1:42 pm
submitted by /u/newsweek [link] [comments]
- Emergency rooms refused to treat pregnant women, leaving one to miscarry in a lobby restroomby /u/Majano57 on April 19, 2024 at 1:12 pm
submitted by /u/Majano57 [link] [comments]
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: That the Beirut Explosion of August 4th, 2020 is considered one of the most powerful artificial non-nuclear explosions in history. It was equivalent to around 1.1 kilotons of TNT and generated an earthquake equivalent to 3.3 in magnitude.by /u/appalachian_hatachi on April 20, 2024 at 1:21 am
submitted by /u/appalachian_hatachi [link] [comments]
- TIL that Walmart's struggle in Germany was linked to excessive employee smiling, which unsettled shoppers unaccustomed to such behavior. This cultural mismatch, along with other company practices, led to Walmart's exit from Germany in the mid-2000s.by /u/BlakeCrong1958 on April 20, 2024 at 1:16 am
submitted by /u/BlakeCrong1958 [link] [comments]
- TIL Frank Hayes, a jockey, died of a heart attack during his final horse race but still won. Unexpectedly, he suffered the attack mid-race, yet his body remained on the horse, crossing the finish line first. Sadly, it was his first and only win throughout his racing career.by /u/Dommondke-162 on April 20, 2024 at 1:12 am
submitted by /u/Dommondke-162 [link] [comments]
- TIL The first time Bobby Kennedy spoke publicly about his brother, President John F. Kennedy's, assassination was while telling a majority-black crowd in Indianapolis that Martin Luther King Jr. had been killed, which is credited with keeping the city calm, while other cities erupted in violence.by /u/Geth_ on April 19, 2024 at 11:14 pm
submitted by /u/Geth_ [link] [comments]
- TIL about Christine Granville, a WW2 spy and Polish aristocrat - Winston Churchill would declare her as his favourite spy. She talks her way into service with M16, gets captured by Germans twice but escapes, and rescues her lover from execution by firing squad.by /u/WeeMaker on April 19, 2024 at 10:40 pm
submitted by /u/WeeMaker [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.
- For the first time in one billion years, two lifeforms truly merged into one organismby /u/ossa_bellator on April 20, 2024 at 1:52 am
submitted by /u/ossa_bellator [link] [comments]
- Last summer was the hottest ever recorded in the United States, and heat-related health emergencies also reached record-high levels in some parts of the country. Heat-related illnesses accounted for a 20% larger share of emergency department visits than they did in the five previous seasonsby /u/Wagamaga on April 19, 2024 at 9:08 pm
submitted by /u/Wagamaga [link] [comments]
- Spanking is associated with detrimental effects on a child’s cognitive, social-emotional, and motor development. The study, conducted across four countries — Bhutan, Cambodia, Ethiopia, and Rwanda — utilizes longitudinal data to provide a more robust analysis than previous studies.by /u/mvea on April 19, 2024 at 7:22 pm
submitted by /u/mvea [link] [comments]
- Researchers have found people maltreated as children are 2.86 times more likely to be hospitalised for alcohol use disorder, and 3.34 times more likely to be admitted for a substance use disorder, by the time they’re 40Y, compared to children who weren’t maltreatedby /u/giuliomagnifico on April 19, 2024 at 5:02 pm
submitted by /u/giuliomagnifico [link] [comments]
- Researchers team has found evidence that the woman’s menstrual cycle is driven by an internal circamonthly timing system than by any other internal or external process, and it’s also weakly but significantly influenced by the 29.5-day lunar cycleby /u/giuliomagnifico on April 19, 2024 at 3:45 pm
submitted by /u/giuliomagnifico [link] [comments]
Reddit Sports Sports News and Highlights from the NFL, NBA, NHL, MLB, MLS, and leagues around the world.
- US Swimmers Have Been Notified That China’s Olympic Gold Medal 800 Free Relay Has Been DQedby /u/KathleenPorter on April 20, 2024 at 2:44 am
submitted by /u/KathleenPorter [link] [comments]
- Washington Mystics the latest WNBA team to relocate game to accommodate Caitlin Clark Fansby /u/kundu123 on April 20, 2024 at 2:40 am
The Mystics home court's capacity taps out at 4,200, while Capital One Arena — home to the Wizards, Capitals, and Georgetown Hoya's Men's Basketball — can fit nearly five times that crowd at some 20,000 spectators. submitted by /u/kundu123 [link] [comments]
- Miami Heat to face Celtics after blowing out Bullsby /u/Oldtimer_2 on April 20, 2024 at 1:32 am
submitted by /u/Oldtimer_2 [link] [comments]
- Miles Russell, 15, youngest to make cut on Korn Ferry Tourby /u/PrincessBananas85 on April 20, 2024 at 12:48 am
submitted by /u/PrincessBananas85 [link] [comments]
- Morikawa in 4-way tie atop RBC Heritage, Scheffler 3 backby /u/Oldtimer_2 on April 19, 2024 at 11:30 pm
submitted by /u/Oldtimer_2 [link] [comments]