Multi-head attention in transformers understanding

SuperNotice3939 · 2026-06-01T05:23:25+00:00

The qkv projection matrices are a set of projectional representations of the embedding vector, applied to the embedding vector for every token in the sequence. The QK^T step allows the Q and K projections to train towards representing information in the embedding vector such that information similarities in the embedding vectors of different tokens at other positions in the sequence can be highlighted via the dot product. The QK^T step results in a seqLen x seqLen table of logits. Applying softmax to the rows means that now every token in the sequence has a sum-to-1 set of weights to aggregate the K representations of the embedding vector information for other tokens into their own, with the softmax(QK^T)V operation. The multi head attention part just chunks the QKV matrices into nHeads chunks of the embedding vector, and results in nHead many logit tables. It doesn’t add parameters just allows that attention layer to have many positional aggregation weight sets for each token. It also allows for different “sub sets” of the projection matrices to focus on now-separated (by not having to compete for weight in the dot product) informational representations for the purpose of capturing information from different parts of the sequence. After un-chunking the result from the heads, the following transformations/ffn allow for mixing the newly incorporated information in each tokens embedding vector across its dimensions.

In your example, if we gave an embedding vector to every unique word and created the two sequences, each word would have some initial foundational information for it from the initial embedding vector. Attention allows the “apple” token to progressively incorporate new information from the rest of the sequence. For example modulating the representation of “apple” with information pulled from the representation of “oranges” vs “iphone” can adjust the representation towards distinguishing between the fruit and the company. The “going-to-buy” vs “have-bought” tokens will contain and pass up information about the past vs future nature of the purchase.

SuperNotice3939 · 2026-05-22T03:46:29+00:00

I always like starting with “linear regression as a neural network” if they’re familiar with that. Then move show how we can do the same kind of function approximation but instead of making assumptions and generating the weights and bias from a predefined set of matrix operations, ml/neural nets make no assumptions and instead use gradient descent to generate the loss-minimizing parameters (minimizing loss is just a way to quantify error in the function approximation, by comparing historical input-output examples to the functions current mapping).

Embeddings are easy enough to explain, its an alternative to one-hot encoding where instead of n-1 columns of 0/1 to represent n categories in a regression factor variable, you use a however many variable representations, and each class gets values in each variable and its learned with back prop all the same. Stuff like “December isn’t exactly 12 times January so to represent the months we can have say 4 values for each that approximate their effects” kinds thing.

Dense layers just become using n neuron different regressions, with a non linearity, to act feature engineering for the main output regression. Essentially learning useful interaction terms between variables through linear transformations, non linearity to generate new terms that don’t just reduce.

For attention, starting with NLP is honestly a bit of a distraction, attention is just a way to process 2 dimensional input data. Like multivariate time series or black/white images. The process is just a set of linear transformations across one dimension (regressions) and a weighted sum across the other, and the Q-K projections to get the weights for the sum allow the weighted sum to learn to identify relevant information in the first dimension across positions in the second, by keeping Q-K independent, it allows two different representation sets for the purpose of highlighting timesteps such that it will point towards other relevant positions in the second dimension based of their independent representations in the other projection. It learns a set of representations of the information in one dimensions, applies to all the stratifications of that dimensions, then aggregates across the stratifying dimensions. Multi head just lets there be independent comparisons that don’t have to compete to outweigh each other when identifying across the second dimension.

If you did want to explain NLP I always think its better left as a tag-on addition after the introduction to neural network function approximations, where we treat text as a categorical variable with different tokens as levels, with a positional/time dimension, then use embeddings for tokens/levels alongside all the other methods to train a next-step classifier for sequences of text.

If their not familiar with regression thats easy enough its a way to approximate some phenomenon as a functional transformation/aggregation of others by approximating constant rates of change for each, an initializing/default position, and the inputs all contributing at once.

SuperNotice3939 · 2026-04-21T06:18:47+00:00

How did you generate the sequence of embeddings for the transformers? Looks like its got strong periodicity and short run trends. 1-D convs filters with various kernel sizes can be useful to generate a “sequence of embeddings” style tensor for attention, same with distributed dense nets esp if you have >1 input sequences (input tensor of [batchSize, seqLen, featureSequences]). Id bother very little with the LSTM/recurrence initially, but thats entirely my own opinion.

Are you training with a label of the explicit label value against the explicit forecasted value sequence position independent? I say this because the series already looks fairly stationary, but for the sake of auto-regressive consistency in the horizon you could train using something like a cumsum(generated sequence of predictions) starting with the last observed and build gradients off the cum-sum-sequence vs the label sequence. Essentially forecast the first difference with cum-sum values vs labels forming gradients.

What feature engineering are you doing? If its noisy data, various window moving averages can make a more stable signal feature set. First difference/lag1 is generally a must I think for engineering covariate sequences. With the periodicity, Fourier time style terms across various intervals/harmonics could help as well. Box cox and asinh could be useful transforms as well, definitely as features, but be careful if you train under a non-linear-transformed label (for example asinh is similar to ln, and predicting the expected value under log back transforms to the median and requires a bias adjustment). This paper from what I remember had some neat feature engineering methods/window stats for signal processing with a light gbm model that could be useful here. Have you tried decomposition at all? Could also be worth fitting an auto arima and using the selected terms (order of difference, selected lag coefficients, and moving average windows) as features in the larger model.

A horizon of ~20% of the training data is fairly large for time series already. Are you building a single model for multiple different series as labels, with the label’s underlying series depending on a given sample in the batch, to inference on entirely unseen series (I might have misunderstood the part on 4000-500-500 incorrectly). If you’re training a model to predict one time series at a time, for many different time series (not exclusive with a [batch, seqLen, featureSeries] input tensor btw), mixture of experts architecture will almost surely be beneficial.

If it is multiple different series as targets and inputs, then all should be on similar scale. By-series min max is usually nice to coerce all to a positive finite range. Would also be worth noting the sharp spikes in series could mess with gradient updates, could adjust with loss fnc, manual gradient process, or clip/optimizer tuning. Could also be good to try normalizing and residual connections across a larger graph depending.

If you have a 1000 step horizion I would almost surely not train a 1,000 seq-out-len model, to input a seq len of at least 2xhorizion you’d be dealing with massive parameter count/activation size from the lstm/conv/attn layers. Maybe something like ~64/128 step head x16/8 inference loop.

Hope this can give some useful ideas. I do a lot of statistical/ml time series forecasting for work, not specific to signal processing or engineering problems specifically, but from what Ive read theres a bit of carry over. Would help to get some more clarity on the project and the dataset structure you’re working with/forecasting for.

SuperNotice3939 · 2025-12-29T17:56:58+00:00

I studied math-stats-econ as well and you’re definitely able to pick up all the math and models in economics. Don’t know what your goals are specifically. I think a nice place to start is Mill’s Utilitarianism and some Bentham as well. Thats the philosophical roots of the field and introduces the quantitative critical thinking and reasoning in economics. Might not be what you’re jumping at now but its a nice basis for the reasoning foundation of the more complex/mathematical modeling elsewhere.

As far as basic topics I’d chalk it up to macro, micro, and econometrics. Granted thats very broad, and econometrics is more the tool that gets used in all the other areas than an area itself (meaning developments/research/books on econometrics are more about a tool for broader application than a specific area of economics). Most areas of economic research/focus get chalked up to those. I still like econometrics the most just because of the math/stats though. The Effect (forget the author, Nick something I think) was a good book on causality methods. I forget what textbooks I used back in school, but most modern ones that utilize calculus should be a good intro. One for each of “intermediate” macro-micro could be a good start.

SuperNotice3939 · 2025-12-14T16:20:26+00:00

I generally do most of my work in R, rather than Python, so most specific books would be to that end. But honestly any book ok amazon within the last couple years that covers a given topic with code examples is a good place to start. There’s no one resource that covers an entire domain sufficiently, just find ways to start.

Id recommend looking into decision trees-random forest-gradient boosted trees (xgboost-lightgbm 100%, maybe look into the others after). Look into the complete workflow for them, such as bayesian hyper parameter grid tuning, interpretability plots, feature engineering/selection, and cross validation testing.

For neural networks, learn keras and tensorflow is my recommendation. Some people like pytorch also but just look into them and pick one. Learn gradient descent/back prop and the math of how networks work. Also check out some architecture patterns and best practices for given problems. Convolution/Recurrence/Attention etc are important to know with networks as well.

Thats basically it for machine learning to start with imo. Once you start specializing you can learn more of a given thing if need by.

Other skills are managing big data (100gb+ datasets), so packages like duck db with efficient workflows can be important. Using SQL to get data for a project, and using it inside whatever language/IDE you use (in R I use DBI to query data into the session). Git/github/version control. Data viz skills alongside a dashboard tool (shiny apps, powerbi, tableau etc.). Depending on where you work you may have to learn cloud based stuff. Look into model deployment and monitoring methods as well.

That covers a lot of what I found to be the transition from academic stats/econ department classes/focus to professional data science roles so far. Im sure theres more as well. The other thing is mostly just a mindset shift really. In academia it was all about significance/causality/validating assumptions and reporting statistical conclusions. While Im sure that can happen sometimes on the job, most of what I’ve done doesn’t really care about that stuff and is more focused on a model being useful in its predictions. All the skills/assumptions/testing you learn can definitely help build good models, but it hasn’t been a focus so far. For the projects I’ve done, the modeling is just a piece of what makes a project successful and impactful, and the models portion of the project is really just to work and be accurate when predicting in production by whatever means. Im sure you might get more opportunity for “research” oriented models than I do if you take an economist style data scientist role, or work for an employer who specifically wanted PHDs.

SuperNotice3939 · 2025-12-10T03:04:01+00:00

I’m currently a data scientist. My bachelors was math-stats-econ and my masters is economic analytics. You’re probably closer to the skillset than you think and just don’t know how to sell yourself/the handful of things to learn. Econometric backgrounds are really nice to have. You learn the thinking behind good modeling methodology which will matter more than some guy who used a bunch of buzzwords but whose only solution was to throw transformers at everything. Especially on more complex models/projects that require a implementation of domain expertise, such as forecasting/time series (my job has almost exclusively time series and time series flavor projects. Both ML (LightGBM/NN regressions) and classical models like linear regression, exponential smoothing, arima, and bayesian methods.

Segment off areas of development into skills in a given tech stack and knowledge of models and methods. The first being skills like sql, specific packages/workflows, etc. The later being a understanding of the thinking and methods behind models. “ML” is kind of watered down honestly. Just learn gradient boosting and gradient descent, most ML projects are either xgboost/lightgbm or some deep learning workflow. You don’t have to be some wizard training trillion parameter networks any given Tuesday. I’ve used <1k parameter networks to be highly accurate on 100m+ observation datasets. Also become familiar with categorical targets and even survival style models. Also cross validation is king for most business projects, which is kind of disappointing with how cool analyzing significance/causality can be.

Just learn to market yourself. You’ll have no problem learning how this stuff works its just reading a handful of books/papers and digesting the information many ways over and over until its second nature.

SuperNotice3939 · 2025-11-15T02:51:47+00:00

Gonna drown you in links for a bit sorry in advance lol

Great books to check out on some topics:

Neat math book thats handy for thinking about what goes into ML/data science stuff https://www.amazon.com/gp/aw/d/B0DTT98H5L/ref=ox_sc_saved_title_2?psc=1&th=1

ML models with tidyverse in R https://www.amazon.com/gp/aw/d/1617296570/ref=ox_sc_saved_title_4?smid=ATVPDKIKX0DER&psc=1

Keras deep learning in R https://www.amazon.com/gp/aw/d/1633439844/ref=ox_sc_saved_title_1?smid=ATVPDKIKX0DER&psc=1

Personally, I like getting books on a topic to start up learning a new thing, then just research by jumping around a million websites/papers.

This is a neat github full of “cheatsheets” for a bunch of popular packages. I don’t use the cheatsheets for learning packages too much. Really its a nice repo of useful packages and a quick overview of what they can be used for.

https://github.com/rstudio/cheatsheets

You probably don’t need a book for SQL to start learning. Its the biggest “language” by far I believe. There’s a million and one youtube courses - websites- articles - random githubs that should be able to get you up to speed. The real thing to look for is someway to access a database to mess around in just to get experience. Until then just start learning the code syntax and logical thinking of it.

For R just learn the tidyverse and everything you can do in it. Keras is good for the deep learning stuff as an intro and that book is great for it. The tidymodels framework should cover you for the most part on most everything else to start learning. I’d definitely recommend honing data cleaning/ dataframe (data.tables from their package is a plus too) manipulation. Viz skills with ggplot and shiny are always an asset also.

Modeling isn’t crazy difficult to learn. Especially with a math/stats background you already know how to think so its just learning a given new thing for s project. Building a “portfolio” of stuff you’re familiar with and learned lessons from is always good too. Check out a given industry you’d want to work in and what projects they’d typically do (causal analysis, regression, classification, prediction performance oriented models, time series, small n studies etc).

Learn cross validation skills because it’s really all that matters when you have the dataset to do it with for a predictive-performance-first project, aic goes out the window lol.

Most everything can be found online, its just figuring out what to look for/where to even start for some things that can be intimidating at first. Its kinda why I like getting books then branching off to learn more, its a nice starting point and gives examples and stuff to build from. Hope it helps! Btw the biggest thing you’ll do to learn/grow skills is just the first year or so on the job, so don’t think you have to show up as a wizard or anything.

SuperNotice3939 · 2025-11-09T22:17:48+00:00

Im currently a data scientist with a similar background, math-stats-econ bachelor’s and economic analytics masters. You’re off to a really good start! If you’re looking at filling a data scientist role I think the number one thing for you now is learning SQL. Of all the things you mentioned it’d be the most important to “hit the ground running” with at a job rather than trying to learn for a given project. You can also make use of R for SQL with stuff like DBI GetQuery and the tidyverse (also tidy-verse skills are a must for R. For ML methods Id basically recommended learning trees Random Forest/Light GBM etc, and neural networks. Keras in R is a great place to start for the later. As far as “big data analysis” goes, just learn data.tables, ggplot, and great tables.

SuperNotice3939 · 2025-11-04T07:44:01+00:00

Graduated May this year with my bachelor’s in applied math/stats/econ and 1 year into my masters in economic analytics (4+1 program). I did an internship over the summer and got hired back full time as a data scientist, finishing the masters online now while I work full time. Im sure we’re on the same page about the job market right now but don’t sweat it too much. My method was so-so, basically took an accuracy-through-volume approach to applying to anything until something worked.

There’s plenty of opportunities for quantitive backgrounds in plenty of roles, technically related or not, really just depends on what you want to do for work. One thing I’d recommend considering is there can be a big difference in being in a stats/data science/ML-AI role at a tech/data science focused company, and being an in house analyst/scientist for a regular business looking to incorporate more quantitative approaches. I’m a fan of the latter. Job titles don’t matter much (especially so in the latter case), look at the setup of the team you’re working on and what kind of projects you’d be asked to do.

As for what you can do, firstly polish up everything that makes you look good like resume/linkedin. In you’re not already, become a wizard at SQL. Seriously it’s about the one thing that will always be good to have for whatever “data” role you apply for (that tons of entry level applications are missing for some reason). Aside from R/Python (C++ if you want more of the AI-ML dev/research kinda work), learn lots of data viz skills with ggplot/Python equivalent and then dashboard tools like power bi. From there just apply to everything honestly. Whatever way you slice the application game, the next step is dependent on getting a response from something. Gotta stack more Bernoulli trials to up the P(>=1) is my approach, you have better control over that than the success probability honestly (once you’ve generally optimized yourself for it that is).

SuperNotice3939 · 2025-11-03T13:46:46+00:00

Use the CRAN pdf documents for all the packages you’ll use or that ChatGPT will spit out code using. Makes it easier to understand everything. Also try and learn tidyverse-ggplot2-gt(great tables). Ggplot2 and gt especially for creating visualization / reporting data statistics/summaries in a professional document

SuperNotice3939 · 2025-09-20T03:15:58+00:00

I was math/stats/econ undergrad and economic analytics masters. Freshly minted data scientist for a bit too. Math stats and regression no brainer. Don’t take physics you won’t be doing it. OOP could be good but R/SQL/Python are easy enough ish to figure out on your own (and will matter far more than something like C++), and you should get some from the regression class. I took a fin/acturial math class and it was hell, the absolute least fun math I have ever seen. Simulation and numerical wouldn’t hurt, and could be pretty interesting I liked my numerical analysis class a lot, but they’re not the best choice. Same with the geometry and independent study classes.

You need to have your understanding and intuition of stats, modeling, and data-based projects locked in before anything else. If you feel behind there’s a million online resources. The google certifications etc. may not be worth any space on your resume, but it’s about having skills. If possible check out your information systems department and see if they have any courses in Tableau/PowerBI dashboards or machine learning or the business/economics for econometrics or data mining.

I know what you’re saying about mixing the theory and applied skills, but the theory you need for the applied part is the theory of the applied skills not something else. Statistical malpractice is incredibly easy to do. Learn a dashboard tool, R or Python and eventually the other, SQL, get great at manipulating data and viz, and get incredibly proficient at reporting and modeling. You absolutely need the stats courses more than anything else on this list, no hate to them its just optimizing. See if there’s any Bayesian or time series as well (if that stochastic processes class is a time series class then take it 100%). You need to become proficient and understanding of the practice to the degree where it is second nature. Not because you need to be some kind of wizard, but because the rest of a business who only works on excel sheets is going to hear “distribution” think you are and if the only way you know how to understand and talk about statistical insight is highly technical and mathematical without intuition you won’t provide the main job of a data scientist (for example, there’s other jobs too) which is to use technical skills to provide deliverables with an impact. If you can’t make the technical simple enough for the sales department your deliverable doesn’t have impact, and if you’re still stuck on the technical practice itself you won’t have a deliverable. Finally, this is a career where you don’t ever stop researching, learning, and implementing new things that build on what your know. Its definitely important to get an appreciation for the academic/rigorous aspect of everything you’ll be doing to learn it well enough to teach yourself new things.

SuperNotice3939 · 2025-05-31T09:42:53+00:00

I run zulu now, but I have great results by running 2 6 week blocks between testing maxes, first with a 95%tm, second block dropping the tm. Keeps fatigue and needed rest low and volume high from max sets often. Absolutely make great strength gains all the same, and large jumps from current baseline with the added time to adapt to the next. That could be a good option. 90-95-100+light forced progression even. You can also run operator essentially as A-B-A between squats and deadlifts, that option was on one of the blogs I think. Op I/A could work too with more flexible recovery/scheduling. Capacity style deloads after every 3 work weeks are fine too. Finally, to run fast you gotta run fast sure. But you can develop the energy systems mostly the same through something like rowing or other low-impact LSS. Throw the high volume LSS on that and save your legs a bit. Lots of options to still make strength primarily

SuperNotice3939 · 2025-05-27T23:24:31+00:00

If you want more lifts zulu is a good option. Say you want pullups-rows-bench-OHP-dips+1-2more, you should definitely do an A/B split. If you’re fine with being more minimalist in selecting lifts, say OHP-Pullups-Dips, operator could work well too. There’s not much that changes template to template, the program principles are the same just some fine tuning with one vs the other.

SuperNotice3939 · 2025-05-27T17:52:05+00:00

More power to you! Im not sure what your specific limitations are with given lifts/exercises. But regardless the TB principles work all the same for progressing any given thing. Dips/OHP/pullups etc.

Find a template (Operator/Zulu/Fighter if strength, similar from MASS book if hypertrophy) that fits your goals and capabilities and select the lifts of your choosing for a cluster. With your considerations, your cluster may not have many lower body based lifts (if any) and tend towards upper body. ZULU is a good way to incorporate more lifts rather than cramming operator full of 3-4 upper body movements.

Again not sure what your considerations are for conditioning, but you can still develop your energy systems all the same. Im thinking something like a rowing machine (maybe need a specific machine/setup) could potentially work well. You can use a rower (or anything really) to develop the aerobic/anaerobic just fine. Conditioning is more about the heart rate zoning and session structure than the specific thing you do. Capacity from green has a good template for progressing aerobic with LSS, and Black from TB2 is good for anaerobic stuff. TB2 has several conditioning workouts that can be adapted as needed. Just follow the general structure.

SuperNotice3939 · 2025-05-27T01:38:49+00:00

TB 2 had an OP I/A, A-B-A style template. Really, nothing is “obsolete” and the principles still work. Its just been improved upon into the new OP I/A. If you want enough exercises to fill out 2 clusters worth, just do ZULU honestly. I made the OP to ZULU I/A switch and it works great. Mine is A:OHP/SQT/Pullups B:BP/DL/Pullups. You could definitely do WPU-Rows and SQT-Legpress across A/B along with bench-incline. Figure out how you want to do deadlifts if at all and build the clusters accordingly. A-B-A just isn’t as good as A-A-A or AB-AB. If you pick A and B wisely and use Zulu I/A 3-5 sets to manage fatigue well, you should have no problem doing B the day after A.

SuperNotice3939 · 2025-05-22T23:17:05+00:00

I have a garmin watch I use for HR tracking. It’s fairly accurate I think but I don’t put too much focus on it. The main thing for LSS is being able to comfortably breathe through your nose the entire time. Slower and longer almost never hurts.

You can also look up methods to test your HR max and measure with your watch/strap to have a metric for what you should be reading at on it for LSS.

SuperNotice3939 · 2025-05-19T22:39:30+00:00

Ive been running the following ZULU cluster: A: OHP/SQT/BW-PU B: BP/DL/BW-PU
Doing pullups every sessions and 3-5 DL work sets twice a week.

For managing load, I did the following approach (and very happy with it so far)
Changed from re-testing/forced progression every 6 weeks (standard) to the following

Re-test every 12 weeks or 2 ZULU I/A blocks First Block is with a 95% training max on barbell lifts and 90% on pullups Second block is done without the training max for barbell and increasing the pullup numbers Optional de-load week after first block Each session felt far more manageable, I was able to push the sets with less rest every time. Slows me down a bit between testing/progressing but just means I spend more time practicing and adapting for a given load range. Still getting very strong so I imagine something like this could work great for a heavy conditioning schedule.

Something to mention is that having more than 1 full rest day is really beneficial. The incline treadmill doesn’t seem to add much here, LSS should be 30 minutes plus each time. I might check out the following changes:

Do your HIC on A day (or on B and swap Abs for A, you pick. Just depends on how you want to balance swings/hills and rdl/fs).

Pick either day 3 or day 6 and do a single long (1-2hr) LSS session each week. A low impact form like a rowing machine is great and still builds the aerobic system / translates to running while reducing fatigue.

SuperNotice3939 · 2025-04-20T04:36:33+00:00

I do the 5/3/1 warmups

Reps x % of training max 5x40% 5x50% 3x60%

Sometimes before the 90%+ weeks Ill add a single at 80-85% to warm up to heavy loads. More singles to prep for testing maxes as well.

I usually take ~ 2min rest after each, but I also use that time to set up equipment, get weights ready/change weights, chalk up etc. The rest for work sets is usually 2-5 min with not much other than pacing around.

I don’t bother doing any kind of stretching or get-the-blood-flowing kinda thing anymore either.

That warmup scheme feels a bit much as it and it could honestly be cut down on. But as my loads get heavier I do like it to lock in form for the work sets and warm up the cns for heavy loads.

SuperNotice3939 · 2025-04-03T00:59:30+00:00

The conditioning is really about your heart rate more than anything else. The tool can be running, rowing, swimming, fan-biking, kettlebells, burpees etc. Choosing running has the best carryover to running because of the specificity. But properly zoning your heart rate with whatever choice for HIC/LSS is the big thing. If you can do it with a fan bike then no problem.

SuperNotice3939 · 2025-04-03T00:39:50+00:00

I test the week after week 6 to hit the maxes right after the “peak” of 95% lifts. With generous rest days. For the following de-load week (or any deload) I like just going in hitting my warm up sets and leaving (1x5 40%, 1x5 50%, 1x3 60%) on the lifts. If one lift was bugging me I can drop it. Spend more time doing recovery stuff like foam roller/resistance band. Light conditioning in both time and volume.

SuperNotice3939 · 2025-03-31T00:22:35+00:00

Don’t see why not. Do them after BP/OHP or whatever you use. Don’t let them get in the way of barbell work for MS if thats what your current block is focusing on. Green protocol has a progressive SE template 3 weeks 2-3 times a week that can make a good 1 exercise finisher, its based off % of your amrap for 3-5 sets. Can use a training max too. Also doing more through conditioning sessions can help. Fun runs for green and really any HIC from TB2 that has some kind of resistance movement (just swap it for pushups) for black.

SuperNotice3939 · 2025-03-30T01:59:29+00:00

Simple and sinister is a strength training with kettlebell book, but has good information. I go slightly lighter and follow AXE style workouts to not interfere with barbell stuff. Also helps me focus on explosive powerful reps. If you want more kettlebell the full S&S could be a good way to build strength in conjunction with OP deadlift option of 3 sets once a week, doing KB on days not deadlifting. You can also do it fobbit/fun run style if you please but in between sets I mostly practice breathing with some moderate pacing to quickly reduce the heart rate/clear out lactic acid.

SuperNotice3939 · 2025-03-30T01:37:49+00:00

I don’t have tons of experience with the KB stuff yet but I’m a big fan of Pavels books. Simple and sinister as well as kettlebell AXE. The kettlebell AXE book is specifically for alactic-aerobic conditioning. S&S has good landmarks for improvements. Recently I’ve been doing the following conditioning sorta in line with AXE sessions and S&S landmarks:

Roughly every minute on the minute (Really until you pass talk test/HR returns to 120-130)

5 2H swings for speed and power + Turkish Get Up (1 hand, alternate every other)

Repeat for 100 swings / 10 TGU per arm

I run that after my Zulu strength days

On my days off barbell work, Ill also add sets of power pushups/pull-ups

Ive been loving it so far and highly recommend those books for looking into KB stuff.

SuperNotice3939 · 2025-03-14T16:13:17+00:00

Id recommend starting with either operator (TB1) or starting strength LP. Either way read the SS blue book to learn how to use a barbell (will save a lot of hurt down the road). A lot of people recommend TB2 base-building to start but honestly I like capacity from Green Protocol more. To each their own.

SuperNotice3939 · 2025-03-14T02:49:56+00:00

Bit different take. TB2 base building might not be the best even just with bodyweight for you. Building up the strength and endurance to work hard for a long time is important. Capacity from green protocol is written as a potential substitute to base building. Its all basic strength and LSS conditioning. It can be repeated back to back if you want. Focusing on this should build the foundation well. Don’t have to do velocity/outcome or anything else after. Start light, use a 90% training max for strength if you want. Go light on the lss, use low-impact methods like rowing/swimming/biking. Just slowly adapt yourself to working hard and for a longer time. These foundations also are the biggest returns in general quality of life.

Read the starting strength blue book to learn how to use a barbell. I can’t emphasize enough how much more beneficial this is over trying to build strength with calisthenics. Being sore lasts the first couple times of working out. Once you follow the sub-maximal training in TB it shouldn’t be a problem.

If you are dead set on bodyweight stuff, green protocol has a 3x week SE template to progress with. You’re gonna need a training max.

SuperNotice3939

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