#NFL Injury Updates #Dolphins De'Von Achane - LP. Unless... setback occurs, data strongly favors playing. Re-injury risk likely low (~10%) given that it took only 2 wks to return to practice #Ravens Isaiah Likely - Out. Wk 2-4 return = likely. Data suggests 4 game ramp up 1/10show more

#NFL Injury Updates #Raiders Brock Bowers - Coach says... playing. Data favors sitting. Practice reports suggests PCL setback. Data strongly suggests major dip if active #Cowboys CeeDee Lamb - Suspect return Wk 6-7. Data favors Wk 7 as most likely. 20% re-injury risk 1/12show more

#NFL Injury Updates #Commanders Jayden Daniels - Playing increases... ACL risk. Video suggests he could if it were playoffs. Wk 3 TBD. Lean slightly toward playing w/brace + rushing dip #Packers Jayden Reed - Clavicle avg 6 wks. Foot 8. Data favors ~Wk 10 + 4 wk ramp up 1/9show more

#NFL Injury Updates #Cowboys CeeDee Lamb - Video suggests... high ankle. Attempting return implies mild. Avg ~ 2 wks. Most miss Wk 4 #Buccaneers Mike Evans - Hamstring. Avg = 2-3 wks. Age 32 + hamstring history predict longer. His reaction suggests high severity. Suspect IR 1/6show more

Tolutau Koula ruled out for Manly this weekend -... couldn’t overcome the AC joint injury he suffered in Magic Round. Typically an extremely painful injury, return becomes mostly pain management issue. Likely grade 2-3 sprain - usual return to play 2-4 weeks from initial injury.show more

HOLEE SHIZZLES‼️ 🚨 The Fulton County Georgia FBI Raid... Affidavit CONFIRMS Election Records in Fulton County's 2020 Vote Count was MANIPULATED 1. Only 16 tabulators out of an expected much larger number were used to generate closing data for about 315,000 ballots across 138 provided poll tapes. This extreme concentration improperly funneled through a small set of machines, breaking chain-of-custody rules and making it easier to alter election results 2. Review of machine logs indicated that memory cards were likely removed from their original tabulators and inserted into different ones to produce or recreate closing poll tapes. This indictated tampering or fabrication of records to cover up discrepancies, as it allows data to be manipulated OUTSIDE the standard process. 3. Many closing poll tapes—essential documents that verify end-of-day vote totals from each polling site—were entirely MISSING from the records provided. Without these, there's no way to confirm that votes weren't added, removed, or changed post-election. FRAUD. 4. It was discovered that the Tabulator's data appeared to cover ballots from several different polling sites, which shouldn't happen under normal procedures. This suggests intentional mixing of data streams, which leads to DUPLICATE votes, misplaced ballots, or hidden errors across precincts. 5. Tabulators showed mismatched or anomalous timestamps in their logs, such as dates and times that didn't align with actual election events. This could indicate backdating, editing, or unauthorized access AFTER polls closed, further hinting at possible manipulation to make records appear consistent. 6. Auditors assisting in the Risk Limiting Audit reported counting purported absentee ballots that had never been creased or folded, as would be required for the ballot to be mailed to the voter and for the ballot to be returned in the sealed envelope requiring the voter’s signature for authentication. Affidavitshow more

HOW TO DODGE EVERY SKILLSHOT IN LEAGUE OF LEGENDS... SO YOU GET ACCUSED OF SCRIPTING - Script in your mind - Draw out how far, wide, fast an ability is relative to your character thats all the easy stuff that I have been preaching already you can find in my free discord for improvement however one thing that League coaches fail to explain is the human aspect of it every game you play in League of Legends, every single person in the game is constantly building their profile in a game on how they operate both sides are constantly trying to mind f*ck each other to land and dodge skillshots. I have broken it down into layers the three layers to dodging are layer 0 - no dodge (unconscious) layer 1 - dodge (conscious) layer 2 - no dodge (conscious) Notice how in the clip in a challenger game below Olaf shoots a layer 0 skillshot, but because I am playing at a layer 1, I dodge his axe. Now the Thresh hook gets a little deeper bare with me, because I built the profile that I will dodge an ability in that moment, he thinks that I won't dodge and is shooting a hook at a layer 2 thinking that I will dodge at a layer 2 also. However I know that he knows I will likely not juke and walk straight so I make the conscious choice to dodge AGAIN playing at a layer 1 resulting in me dodging the hook, of course he could be accounting for my tumble but the point still stands. There are many deeper things to consider like zoning abilities, environment etc but you generally want to always play at a layer 1 until you gain more data in a game to adapt. However one thing that always stays true throughout my 13 years of playing League is in teamfights that have gone on for awhile, human beings tend to panic and default to layer 0 of shooting abilities, so if your able to operate at layer 1 as a teamfight progresses, you will likely dodge that one final skillshot that wins you the game. study the saskio wayshow more

This new skill for Hermes Agent wipes your personal... data off hundreds of broker sites for free, hands-off. It's called Unbroker. I’ve been testing it this week and it's incredible. Here is the full setup: 1. Install it by typing: hermes skills install official/security/unbroker 2. Connect browser automation: Wire up Browserbase so the agent can drive real broker opt-out forms. 3. Do the intake: Give consent, then feed it your info. 4. Fire one prompt: "Use the unbroker skill to remove my data from data brokers. Here is my consent. Run it hands-off." Hundreds of data brokers publish your name, addresses, phone, email, even your relatives. Anyone can find where you live in about ten seconds. The law says brokers have to delete your data if you ask. There's just no easy bulk button for it. Services charge $100’s a year to file the exact requests you could submit yourself for free. Unbroker does the same thing locally, and it's free. It fans out sub-agents, scans every broker site, drafts or sends opt-out emails, and only comes back to you for the ones that need a human. The best part is it loops. Brokers re-list you over time, so it schedules rechecks and re-files on its own. Set it once, it keeps working. One skill, one prompt, dozens of broker sites handled.show more

This is why Tesla makes the safest vehicles in... the world: • All Tesla models have received five-star safety ratings from the National Highway Traffic Safety Administration. • Built from the ground up with an all-electric architecture, resulting in low rollover risk and reduced occupant injury probability. • Uses anonymous/aggregated real-world data from millions of miles driven to enhance safety via over-the-air updates and inform future vehicle designs. • Battery packs designed to isolate and vent heat away from the cabin; historically (2012–2020 U.S. data), Tesla vehicles ~10x less likely to experience fire per mile than average gas vehicles. • Before a crash occurs, Tesla uses the front cameras to observe the scenario and prepare the seat belt system to react faster and with adequate force and timing when impact occurs, reducing the amount of slack in each seat belt. • Tesla’s advanced airbags are tuned to deploy according to crash type and different-sized occupants. This includes active venting that changes the amount of pressure within the inflated cushion by releasing gas according to the expected crash severity. • When a serious collision is detected, the hazard lights will turn on to increase your visibility and doors will automatically unlock for emergency access. At the same time, your Tesla will automatically contact emergency services to get help to you as quickly as possible. • Tesla vehicle structures are designed to cushion and protect the battery in the event of an accident. Onboard systems will automatically disconnect the high-voltage battery upon impact. If a battery fire does occur, the battery pack is designed to spread heat away from the cabin to protect occupants.show more

🚨CLOSE TO 10 INCHES OF RAIN SO FAR -... TOTALS TRENDING HIGHER - Significant Flooding Rain has already fallen across Southern California, with parts of Santa Barbara and the LA area ALREADY seeing up to 9 inches or more. 🌧️ More heavy rain is coming today, with another 4 to 8 inches possible, which will only worsen ongoing flooding issues. 🌬️ Strong winds have arrived as well, with gusts over 60 mph reported in many areas. Power outages are becoming more likely, especially with Christmas right around the corner. And keep this in mind. Today is only Wave 1 of 2. Another round of rain, wind, and heavy mountain snow arrives Christmas Day. Hang in there, California. We’re praying for you 🙏show more

I wanted to take a moment to talk about... my early stages in golf and hopefully this helps someone out there getting into the game. This video is from 2013, around 1 year into golf and I was shooting mid- low 90s. My Dad was a teaching pro so the fundamentals came easy. However, all my friends at the time played since they were 5 years old and I felt a ton of pressure trying to “catch up”. Golf never seemed to come easy for myself. I struggled really bad for 2-3 years before I saw any true progress. I was never a “natural” at the game At this time, my main goal was to play college golf so a lot of progress needed to take place. So, we moved to Florida as a family for my dads job and that’s when everything changed. I began to practice each day for 3-5 hours. I realized since I wasn’t a natural, I had to work harder then everyone. My scores began to drop into the 70s consistently after 3-4 years of playing. When I started seeing these results I got even more motivated. To play in college I needed to be posting low scores in competitive junior tournaments. These environments I believe took my game even to another level. I began shooting in the low 70s and 60s on a consistent basis. Getting to this point easily took 5 years of grinding, while some of my friends it took 2-3 years. I did end up playing 4 years of D2 college golf and posted a lot of scores I’m super proud of. My overall point is people progress at different speeds in this game. I realize not everyone can’t practice 5 hours a day. But if you haven’t seen results right away, or even years into the game, never give up. Something might click and everything could change!show more

💸💵 $25 GIVEAWAY 💵💸 To Enter: ❤️ 1.) LIKE... ♻️ 2.) RETWEET (Must Follow) $25 Giveaway to one person once this PrizePicks play smacks tonight. Tatis is lighting it up. He’s gone for 5, 1 and 8 HRR in DR’s 3 games in the WBC and should continue that tonight. DR is scoring over 10+ runs a game and he’s leading off, giving him tons of opportunity to score and get production in. Dejounte has been very, very consistent in his return so far this season. Across the board, and especially in getting to the line. He’s averaging 4.17 FTM so far and is over in 5/6 (83%) of games, only missing in his first game back this season.show more

[VAE] by Hand ✍️ A Variational Auto Encoder (VAE)... learns the structure (mean and variance) of hidden features and generates new data from the learned structure. In contrast, GANs only learn to generate new data to fool a discriminator; they may not necessarily know the underlying structure of the data. The International Conference on Learning Representations (ICLR) this year announced its first ever "Test of Time Award" to recognizes the VAE paper, published 10 years ago. This exercise demonstrates how to calculate a VAE by hand. [1] Given: ↳ Three training examples X1, X2, X3 ↳ Copy training examples to the bottom ↳ The purpose is to train the network to reconstruct the training examples. ↳ Since each target is a training example itself, we use the Greek word "auto" which means "self." This crucial step is what makes an autoencoder "auto." [2] Encoder: Layer 1 + ReLU ↳ Multiply inputs with weights and biases ↳ Apply ReLU, crossing out negative values (-1 -> 0) [3] Encoder: Mean and Variance ↳ Multiply features with two sets of weights and biases ↳ 🟩 The first set predicts the means (𝜇) of latent distributions ↳ 🟪 The second set predicts the standard deviation (𝜎) of latent distributions [4] Reparameterization Trick: Random Offset ↳ Sample epsilon ε from the normal distribution with mean = 0 and variance = 1. ↳ The purpose is to randomly pick a offset away from the mean. ↳ Multiply the standard deviation values with epsilon values. ↳ The purpose is to scale the offset by the standard deviation. [5] Reparameterization Trick: Mean + Offset ↳ Add the sampled offset to predicted mean ↳ The result are new parameters or features 🟨 as inputs to the Decoder. [6] Decoder: Layer 1 + ReLU ↳ Multiply input features with weights and biases ↳ Apply ReLU, crossing out negative values. Here, -4 is crossed out. [7] Decoder: Layer 2 ↳ Multiply features with weights and biases ↳ The output is Decoder's attempt to reconstruct the input data X from reparameterized distributions described by 𝜇 and 𝜎. [8]-[10] KL Divergence Loss [8] Loss Gradient: Mean 𝜇 ↳ We want 𝜇 to approach 0. ↳ A lot of math called SGVB simplifies the calculation of loss gradients to simply 𝜇 [9,10] Loss Gradient: Stdev 𝜎 ↳ We want 𝜎 to approach 1. ↳ A lot of math simplifies the calculation to 𝜎 - (1/ 𝜎) [11] Reconstruction Loss ↳ We want the reconstructed data Y (dark 🟧) to be the same as the input data X. ↳ Some math involving Mean Square Error simplifies the calculation to Y - X.show more

I took this with the intention of sharing it... earlier but wimped out. Changed my mind though because I figured it might help motivate someone. I started running again 4 weeks ago after almost no running in the last 10+ years. In just a month, I knocked out an unofficial half marathon. Not only that, but it felt easy enough to talk 11.5 miles in without being winded. I’m not running fast, in fact I’m really slow at around a 12min/mile pace, but that’s the point I want to make. Running at a low heart rate does amazing things. If you want to try out this style, look up zone 2 training. Or, just go out and do some extended exercise (walk/jog) while keeping your heart rate around 130-145. Walk if you go over that upper end, even I had to when I started. Stay consistent, and you’ll see improvement relatively quickly.show more

🚨 DON'T BUY $BTC RIGHT NOW First, $BTC will... fill the $70K FVG Then it'll create a massive Bull Trap and convince everyone uptrend is back Only after that will the final flush begin Here's my plan: 1. Local correction into $64K area (DONE) ✅ 2. Then a rally to $70K 3. 10-14 days of distribution 4. Then a breakdown below $58K 5. Flush to $48K 6. Final bottom test in $39K-$45K zone As $BTC pushes higher, high-cap alts could rally another 10-30% That's exactly when you don't want to fall into trap... I believe final phase of Bear Market hasn't started yet The biggest sell-off is likely over next 30-90 days IMPORTANT: I was one of first to call $BTC drop from $83K My next call could be the biggest one in months TURN NOTIFICATIONS ON BEFORE IT'S TOO LATE...show more

[Dropout] by hand ✍️ Dropout is a simple yet... effective way of reducing overfitting and improving generalization. This by-hand exercise lets students practice calculating dropout, thereby gaining insight into its inner workings. As an additional bonus, students get to practice calculating the gradients of the Mean Square Error (MSE) loss. After the practice, students are often surprised by how simple it is. -- 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 -- 1. Linear(2,4) 2. ReLU 3. Dropout(0.5) 4. Linear(4,3) 5. ReLU 6. Dropout(0.33) 7. Linear(3,2) -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- 🏋️ Training [1] Given ↳ A training set of 2 examples X1, X2 [2] 🟧 Random: p > 0.5 ↳ Draw 4 random numbers ↳ For each random number, if it is above 0.5, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ╳, ◯, ╳] [3] 🟧 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 2 ↳ Set the diagonal based on [◯, ╳, ◯, ╳], where ◯ = 2 and ╳ = 0 ↳ The purpose is to drop the 2nd and the 4th nodes, and scale the remaining two nodes by 2. [4] 🟦 Random: p > 0.33 ↳ Draw 3 random numbers ↳ For each random number, if it is above 0.33, we keep and denote it as ◯. Otherwise we drop and denote it as ╳. ↳ The result is [◯, ◯, ╳] [5] 🟦 Dropout: Matrix ↳ Calculate the scaling factor: 1 / (1-p) = 1.5 ↳ Set the diagonal based on [◯, ◯, ╳], where ◯ = 1.5 and ╳ = 0 ↳ The purpose is to drop the 3rd node, and scale the remaining two nodes by 1.5. [6] Feed Forward ↳ Now we have all the matrices ready across the layers, perform the feed forward pass by calculating a series of matrix multiplications from the top to the bottom ↳ ReLU activation function is applied along the way to set negative feature values to zeroes, denoted by ╳. ↳ The outputs are Y. [7] 🟥 Loss Gradients of Mean Square Error (MSE) ↳ The formula is 2 * (Y - Y') ↳ First we calculate Outputs (Y) - Targets (Y') ↳ Second we multiply each element by 2 [8] Update Weights ↳ Use loss gradients to start back propagation ↳ Update some weights (light red) ↳ The values of the new weights are for demonstration purpose only, not based on real calculation. 🔍 Inference [9] Deactivate Dropout ↳ We set both dropout matrices to identity matrices ↳ The effect is to keep all the features as is. [10] Feed Forward ↳ Take the forward pass to make predictions about unseen data.show more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more