What is the Best Machine Learning Algorithms for Imbalanced Datasets

Machine Learning Algorithms and Imbalanced Datasets

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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. 

What is the Best Machine Learning Algorithms for Imbalanced Datasets
What is the Best Machine Learning Algorithms for Imbalanced Datasets

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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:

  1. 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.
  2. 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.
  3. 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.
  4. 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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  1. 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.
  2. 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.
  3. 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.
  4. 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] are there any reading groups/journal clubs for ML/AI related topic?
    by /u/Illustrious-Pay-7516 (Machine Learning) on May 20, 2024 at 10:04 pm

    Hi, does anyone know if there are any reading groups/journal clubs where people share book chapters or papers regularly? It might be good to have some people reading the same book/paper share their ideas/thoughts if possible. Thanks! submitted by /u/Illustrious-Pay-7516 [link] [comments]

  • [D] Hypothetically, in the future: How would you make an accurate and detailed digital twin of Earth using AI?
    by /u/SnowmanRandom (Machine Learning) on May 20, 2024 at 9:42 pm

    The reason I think it would be possible soon is that we already have so much data on the internet. As a thought experiment: If we had billions of human 3d-artists and gave them access to all the data (text, photos, maps, video etc), I think they could rebuild the entire planet very accurately in 3d (because their brains have lots of experience from the real world and know how to relate 2d images to a 3d space etc). They could even use their massive real world experience to fill in very plausible details where data is missing. This would probably take many many years, but I think it means that (in theory) a properly trained AI-model could do it much faster (and more accurately). How would you tackle this challenge? How would such an AI-model be trained? https://preview.redd.it/9hh6hq6cin1d1.jpg?width=700&format=pjpg&auto=webp&s=4b935bc18ea4a0078ded2843ceebcb976b37204a PS! I know Google street view and maps exist, but it would be cool if we could get much more details and accuracy up close. To the point of being able walk around on ground level and looking almost exactly like the real world. I imagine it could be used for lots of applications (including simulators and GTA7/8 haha). submitted by /u/SnowmanRandom [link] [comments]

  • [D] - Can multimodal models tell images apart from text? Like if a text token and an image token are close vectors, will the model be able to "tell" if it is reading or seeing?
    by /u/30299578815310 (Machine Learning) on May 20, 2024 at 7:44 pm

    I ran into this doing some work with multimodal models. It seemed like they couldn't tell which part of the information was from the text vs the image portions of an input. Is there any research on this? submitted by /u/30299578815310 [link] [comments]

  • [Discussion] Computer Vision Lie Detection?
    by /u/Thomas-Gerard-1564 (Machine Learning) on May 20, 2024 at 7:25 pm

    I can find lots of examples of lie detection with NLP, but I'm wondering if anyone has come across computer vision data for lie detection, or a data set that could be used for that purpose. In a perfect world, the data would probably be in video format, but I suppose it's possible it could be done with facial recognition data too. I recall a news article I found a few years ago (can't find it now) where an ML model had been built to detect lies based on facial expressions. I did find a much more recent video (skip to 2:04 for the relevant bit) where Israel had developed a technique using facial muscle sensors, and this may be the original innovation I had read about, since I believe the model in the older article was also in use by the Israeli military. submitted by /u/Thomas-Gerard-1564 [link] [comments]

  • [D] what is the advantage and disadvantage of using custom neural network for a secondary meta learner of a stacked ensemble, than using a traditional classifier?
    by /u/Mental_Ad_9152 (Machine Learning) on May 20, 2024 at 3:37 pm

    Is this even possible? Every journals I read they only mention the usage of classifiers like xgboost, logistic regression etc. however, when I ask on chatgpt it recommends the neural network ensemble as the most effective secondary meta learner for stacking. submitted by /u/Mental_Ad_9152 [link] [comments]

  • [D] Transliteration + translation of comments on Instagram app
    by /u/ts_aditya (Machine Learning) on May 20, 2024 at 3:31 pm

    Is it just me or does anyone notice the stark improvement in the quality of translation - especially translation from languages that is written using english characters (transliteration + translation). Wonder what kind of models they are using that led to the sudden improvement submitted by /u/ts_aditya [link] [comments]

  • Predictive binary ml - variable selection [D]
    by /u/wiktor2701 (Machine Learning) on May 20, 2024 at 12:39 pm

    Hi, I have been working on a binary predictive model for months. I am trying to optimise the variable selection. I have around 12. Using lasso, best subset, trees etc is not what I’m looking for. I am trying to loop through all the combinations but it is not only difficult to code, but I also doubt my laptop will be able to handle it if I code it. Any tips ? submitted by /u/wiktor2701 [link] [comments]

  • [P] SDG- adds support for GPT-based synthetic data generation for single table
    by /u/hitszids (Machine Learning) on May 20, 2024 at 11:42 am

    https://github.com/hitsz-ids/synthetic-data-generator submitted by /u/hitszids [link] [comments]

  • [D] Has anyone worked with rembg (python)?
    by /u/PrinceOfBeauty (Machine Learning) on May 20, 2024 at 9:00 am

    def remove( data: Union[bytes, PILImage, np.ndarray], alpha_matting: bool = False, alpha_matting_foreground_threshold: int = 240, alpha_matting_background_threshold: int = 10, alpha_matting_erode_size: int = 10, session: Optional[BaseSession] = None, only_mask: bool = False, post_process_mask: bool = False, bgcolor: Optional[Tuple[int, int, int, int]] = None, *args: Optional[Any], **kwargs: Optional[Any] ) -> Union[bytes, PILImage, np.ndarray]: """ Remove the background from an input image. This function takes in various parameters and returns a modified version of the input image with the background removed. The function can handle input data in the form of bytes, a PIL image, or a numpy array. The function first checks the type of the input data and converts it to a PIL image if necessary. It then fixes the orientation of the image and proceeds to perform background removal using the 'u2net' model. The result is a list of binary masks representing the foreground objects in the image. These masks are post-processed and combined to create a final cutout image. If a background color is provided, it is applied to the cutout image. The function returns the resulting cutout image in the format specified by the input 'return_type' parameter. Parameters: data (Union[bytes, PILImage, np.ndarray]): The input image data. alpha_matting (bool, optional): Flag indicating whether to use alpha matting. Defaults to False. alpha_matting_foreground_threshold (int, optional): Foreground threshold for alpha matting. Defaults to 240. alpha_matting_background_threshold (int, optional): Background threshold for alpha matting. Defaults to 10. alpha_matting_erode_size (int, optional): Erosion size for alpha matting. Defaults to 10. session (Optional[BaseSession], optional): A session object for the 'u2net' model. Defaults to None. only_mask (bool, optional): Flag indicating whether to return only the binary masks. Defaults to False. post_process_mask (bool, optional): Flag indicating whether to post-process the masks. Defaults to False. bgcolor (Optional[Tuple[int, int, int, int]], optional): Background color for the cutout image. Defaults to None. *args (Optional[Any]): Additional positional arguments. **kwargs (Optional[Any]): Additional keyword arguments. Returns: Union[bytes, PILImage, np.ndarray]: The cutout image with the background removed. """ def remove( data: Union[bytes, PILImage, np.ndarray], alpha_matting: bool = False, alpha_matting_foreground_threshold: int = 240, alpha_matting_background_threshold: int = 10, alpha_matting_erode_size: int = 10, session: Optional[BaseSession] = None, only_mask: bool = False, post_process_mask: bool = False, bgcolor: Optional[Tuple[int, int, int, int]] = None, *args: Optional[Any], **kwargs: Optional[Any] ) -> Union[bytes, PILImage, np.ndarray]: """ Remove the background from an input image. This function takes in various parameters and returns a modified version of the input image with the background removed. The function can handle input data in the form of bytes, a PIL image, or a numpy array. The function first checks the type of the input data and converts it to a PIL image if necessary. It then fixes the orientation of the image and proceeds to perform background removal using the 'u2net' model. The result is a list of binary masks representing the foreground objects in the image. These masks are post-processed and combined to create a final cutout image. If a background color is provided, it is applied to the cutout image. The function returns the resulting cutout image in the format specified by the input 'return_type' parameter. Parameters: data (Union[bytes, PILImage, np.ndarray]): The input image data. alpha_matting (bool, optional): Flag indicating whether to use alpha matting. Defaults to False. alpha_matting_foreground_threshold (int, optional): Foreground threshold for alpha matting. Defaults to 240. alpha_matting_background_threshold (int, optional): Background threshold for alpha matting. Defaults to 10. alpha_matting_erode_size (int, optional): Erosion size for alpha matting. Defaults to 10. session (Optional[BaseSession], optional): A session object for the 'u2net' model. Defaults to None. only_mask (bool, optional): Flag indicating whether to return only the binary masks. Defaults to False. post_process_mask (bool, optional): Flag indicating whether to post-process the masks. Defaults to False. bgcolor (Optional[Tuple[int, int, int, int]], optional): Background color for the cutout image. Defaults to None. *args (Optional[Any]): Additional positional arguments. **kwargs (Optional[Any]): Additional keyword arguments. Returns: Union[bytes, PILImage, np.ndarray]: The cutout image with the background removed. """ I'm trying to remove the background from an image, but it's not entirely successful. Pieces of the background remain. I tried with different values for alpha_matting_foreground_threshold, alpha_matting_background_threshold and alpha_matting_erode_size but I did not have an improved result. Most of the time it was the same result as before, when alpha_matting was False. I don't understand what these parameters are, how they help, what values they can take. I thought that from 0 to 255 but I can also accept 400. I really don't understand the logic and please help me with a good explanation. submitted by /u/PrinceOfBeauty [link] [comments]

  • [R] Medical Language Agent Simulation (Benchmark)
    by /u/panthsdger (Machine Learning) on May 20, 2024 at 3:01 am

    Website: https://agentclinic.github.io/ Arxiv: https://arxiv.org/pdf/2405.07960 TLDR: AgentClinic turns static medical QA problems into agents in a clinical environment (doctor, patient, medical devices) in order to present a more clinically relevant challenge for medical language models. Abstract: Diagnosing and managing a patient is a complex, sequential decision making process that requires physicians to obtain information---such as which tests to perform---and to act upon it. Recent advances in artificial intelligence (AI) and large language models (LLMs) promise to profoundly impact clinical care. However, current evaluation schemes overrely on static medical question-answering benchmarks, falling short on interactive decision-making that is required in real-life clinical work. Here, we present AgentClinic: a multimodal benchmark to evaluate LLMs in their ability to operate as agents in simulated clinical environments. In our benchmark, the doctor agent must uncover the patient's diagnosis through dialogue and active data collection. We present two open benchmarks: a multimodal image and dialogue environment, AgentClinic-NEJM, and a dialogue-only environment, AgentClinic-MedQA. Agents in AgentClinic-MedQA are grounded in cases from the US Medical Licensing Exam~(USMLE) and AgentClinic-NEJM are grounded in multimodal New England Journal of Medicine (NEJM) case challenges. We embed cognitive and implicit biases both in patient and doctor agents to emulate realistic interactions between biased agents. We find that introducing bias leads to large reductions in diagnostic accuracy of the doctor agents, as well as reduced compliance, confidence, and follow-up consultation willingness in patient agents. Evaluating a suite of state-of-the-art LLMs, we find that several models that excel in benchmarks like MedQA are performing poorly in AgentClinic-MedQA. We find that the LLM used in the patient agent is an important factor for performance in the AgentClinic benchmark. We show that both having limited interactions as well as too many interaction reduces diagnostic accuracy in doctor agents. submitted by /u/panthsdger [link] [comments]

  • [R] What is the state-of-art of model parallelism ?
    by /u/Various_Protection71 (Machine Learning) on May 20, 2024 at 12:51 am

    Is it easy to implement model parallelism with common frameworks like PyTorch and Tensorflow? It depends on the model architecture? What are the most used approaches on model parallelism ? submitted by /u/Various_Protection71 [link] [comments]

  • [P] Simplified PyTorch Implementation of AlphaFold 3
    by /u/csozboz (Machine Learning) on May 19, 2024 at 10:48 pm

    submitted by /u/csozboz [link] [comments]

  • [D] Fine-Tuning LLaVA: Duration and Configuration?
    by /u/NbaWM2394 (Machine Learning) on May 19, 2024 at 10:09 pm

    Hi everyone, I'm planning to fine-tune the LLaVA model and am curious if anyone here has experience with this. Specifically, I'm looking to understand: The size of your dataset (number of images and annotations). How long the fine-tuning process took. Your hardware setup (GPUs, CPUs, RAM). Any specific configuration settings you used. Thanks in advance submitted by /u/NbaWM2394 [link] [comments]

  • [D] What role do you think machine learning will play in fields like computational biology and bioinformatics in the coming years?
    by /u/RawCS (Machine Learning) on May 19, 2024 at 8:19 pm

    I believe that computation biology and bioinformatics are going to be adopting ML work more and more, and I’m quite excited to see what advancements are made. I think it is going to open up a whole new world in terms of matching diseases to current medications that could potentially be used off label. What other things should we be on the lookout for? Who are some researchers working in this world? submitted by /u/RawCS [link] [comments]

  • [D] Are LLM observability tools really used in startups and companies?
    by /u/WolvesOfAllStreets (Machine Learning) on May 19, 2024 at 7:50 pm

    There are many LLM observability and monitoring tools launching every week. Are they actually used by real startups and companies? These tools seem to do one or a combination of the following: - monitor LLM inputs and outputs for prompt injection, adversarial attacks, profanity, off-topic content, rtc - monitor LLM metrics over time such as cost, latency, readability, output length, and custom metrics (tone, mood, etc), drift - prompt management: a/b testing, versioning, gold standard set What have you observed — in real companies who have their own LLM-powered features or products, do they used these tools? submitted by /u/WolvesOfAllStreets [link] [comments]

  • [P] Title: I created a Neural Network to quickly detect spoken vowels 20 times per second
    by /u/Zeno_3NHO (Machine Learning) on May 19, 2024 at 7:46 pm

    ​ Quick disclaimer: I am aware that there is an internaltional standard for labeling the diferent recognized speech sounds (phonemes), but I wanted ASCII or extended ASCII for programming simplification, so I use a different nomeclature. Besides, it's easier for me to recognize and read. -Please forgive me ​ So I have often wondered about the real rules that govern speech that people use. For instance using something similar to a "glottal stop" to end words like "don't" and "that". The "t" is not pronounced. Or how "r" is almost always used as a vowel (in american english). My favorite examples are "fur", "fir", and "-fer". All three are pronounced identically and the typical "i,u,e" vowels are not pronounced at all. Its just pronounced "fr". ​ One day I was looking at a spectrograph of my voice, and I noticed some patterns. Vowels like "ah" in "stop" and "Bob" look very different from other vowels like "ee" in "green" and "bee". When we speak, there is the most prominant lowest frequency called the "fundamental", and there are many other frequencies that are multiples of that frequency called "harmonics". The sound "ah" has high volume on many of the harmonics, but the sound "ee" has a big gap where the harmonics are much much smaller. Every different vowel had its own combination of different harmonic values. ​ So I tried to create a set of rules by hand to classify different frequency patterns as different vowels. I could easily tell them apart by looking at them, but would the rules hold up to the test? So I made a computer program to guess different vowels, but it was not good. There are so many knobs to turn to create the different rules. And if there is variability, then I would also have to go through and determine all of the different ranges which would make the rules much more complex. ​ I started to do it by hand and tweak values, see how it worked, and then tweak the values again, etc, etc. ​ Thats when it hit me! I'm doing what a neural network trainer does. I could use one to do this for me! ​ So I researched the nitty gritty of getting one setup, recorded a lot of data (~45 minutes worth) and trained the model. It took a few days to figure out some problems, but I eventually got it working. ​ I used python and the tensoflow+keras library suite to create and train the neural network, Pyaudio for recording training data and realtime audio, numpy for data analysis. The neural network had 264 input nodes, 100 intermediate nodes, and 13 output nodes (one node for "no vowel", and 12 for the different vowels). The frequency calculation finishes within 1milisecond, and the neural network finishes within 2 milisecond as well on my hardware (intel i3-1115G4 at 4GHz). It spends more of its time on listening for audio than it does computing the answer. I found best results by running the loop 20 times per second (50ms) but I have also gotten it to run at 50 times per second (20ms), but it struggles on one or two vowels. ​ Here is a list of the different vowels that it recognizes ​ ӑ aa cat, 1 ŏ ah stop, 2 ē = ee green, 3 ō = oh gross, 4 oo = oo mood blue goose, 5 ĭ = ih sit,6 ā = ay stay, 7 ĕ = eh pet, 8 ŭ = uh bump, 9 o͝o = ou would could should took, 10 r̃ = (i chose this symbol) ur fur fir fer rural, 11 L' = LL travel left rural, 12 submitted by /u/Zeno_3NHO [link] [comments]

  • [D] SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion
    by /u/rezayazdanfar (Machine Learning) on May 19, 2024 at 5:47 pm

    Happy to share my latest Medium article about Time Series Forecasting."SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion" It is about SOFTS, an innovative MLP-based model that utilizes the novel STar Aggregate-Dispatch (STAD) module to centralize channel interactions, achieving superior forecasting performance with linear complexity. Unlike traditional methods that struggle with the trade-off between robustness and complexity, SOFTS efficiently captures channel correlations, paving the way for scalable and accurate predictions across various fields like finance, traffic management, and healthcare. https://medium.com/towards-artificial-intelligence/softs-efficient-multivariate-time-series-forecasting-with-series-core-fusion-0ac40d2adcd2 submitted by /u/rezayazdanfar [link] [comments]

  • [D] Does DSPy actually change the LM weights?
    by /u/chessnudes (Machine Learning) on May 19, 2024 at 4:58 pm

    I always thought it's essentially glorified and structured prompt engineering (very useful still IMO), but it also claims in the docs that it fine-tunes and changes LM weights, and then absolutely refuses to elaborate on this in any of the sections in their docs. I don't even understand how it can change the actual parameters of the LM, especially if we're using third party API calls for the LMs. By LM weights, I assume it means the weights of the last layers of the transformer model. When they describe optimizers, they say "DSPy introduces new optimizers, which are LM-driven algorithms that can tune the prompts and/or the weights of your LM calls, given a metric you want to maximize." Am I misunderstanding what they mean by LM weights? I'm sorry if this is a stupid question, but I just can't seem to find any information about this. Thanks in advance! submitted by /u/chessnudes [link] [comments]

  • [D] How did OpenAI go from doing exciting research to a big-tech-like company?
    by /u/UnluckyNeck3925 (Machine Learning) on May 19, 2024 at 4:46 pm

    I was recently revisiting OpenAI’s paper on DOTA2 Open Five, and it’s so impressive what they did there from both engineering and research standpoint. Creating a distributed system of 50k CPUs for the rollout, 1k GPUs for training while taking between 8k and 80k actions from 16k observations per 0.25s—how crazy is that?? They also were doing “surgeries” on the RL model to recover weights as their reward function, observation space, and even architecture has changed over the couple months of training. Last but not least, they beat the OG team (world champions at the time) and deployed the agent to play live with other players online. Fast forward a couple of years, they are predicting the next token in a sequence. Don’t get me wrong, the capabilities of gpt4 and its omni version are truly amazing feat of engineering and research (probably much more useful), but they don’t seem to be as interesting (from the research perspective) as some of their previous work. So, now I am wondering how did the engineers and researchers transition throughout the years? Was it mostly due to their financial situation and need to become profitable or is there a deeper reason for their transition? submitted by /u/UnluckyNeck3925 [link] [comments]

  • Multimodal AI from First Principles - Most fundamental approaches [D]
    by /u/AvvYaa (Machine Learning) on May 19, 2024 at 4:12 pm

    Sharing a video I made on some of the most critical and fundamental building blocks to train Multimodal models for the past decade or so… hope you enjoy if the topic interests you! submitted by /u/AvvYaa [link] [comments]

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List of Freely available programming books - What is the single most influential book every Programmers should read



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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
zCanadian Quiz and Trivia, Canadian History, Citizenship Test, Geography, Wildlife, Secenries, Banff, Tourism

Top 1000 Africa Quiz and trivia: HISTORY - GEOGRAPHY - WILDLIFE - CULTURE - PEOPLE - LANGUAGES - TRAVEL - TOURISM - SCENERIES - ARTS - DATA VISUALIZATION
Africa Quiz, Africa Trivia, Quiz, African History, Geography, Wildlife, Culture

Exploring the Pros and Cons of Visiting All Provinces and Territories in Canada.
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
Exploring the Advantages and Disadvantages of Visiting All 50 States in the USA


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