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⬇️ PG SKILL-BUILDING WORKOUT ⬇️ 1. V-Dribble Passing Series (10x each way) - double cross, wrap around bounce pass - BTL>BTB, btb pass - alternate the combos/passes 2. P&R reads/passing/scoring (5x each way) - Backside hook passes, mid-range pull ups - Reverse pivots, relocate 3s 3. Touch work/jumpers (5x... show more
20,293 次观看 • 9 个月前 •via X (Twitter)
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![ATG School Policies 1-5: [I’m not trying to reinvent schooling. There are just a handful of things I believe in which I haven’t seen in any school I’ve been around as a student, coach, or parent.] Policy #1: Each student gets to be responsible for growing some of their own food, no matter how small, and THROUGHOUT schooling (not just a quickie project here or there). Policy #2: Minimum 1:1 ratio of time NOT SITTING IN THE CLASSROOM. What you do with this is up to you. There are so many real world skills, sports, gardening, music, etc. The strict ratio in the school day is the key for me. Common sense and personal interests can take it from there. Policy #3: Daily time to read whatever you want to read about. The biggest barrier for my reading was INTEREST. Be there to ensure the book is at their level, and to help them if they don’t understand something. Other than that, LET THEM ENJOY READING, ALL THE WAY THROUGH SCHOOL, not just in early years. Policy #4: High school is a 50/50 bridge to winning in real life. Mornings are for actual work, making and SAVING UP MONEY. Afternoons are for learning finances and professional skills of YOUR INTEREST. With average work, you’ll finish school with $25,000-$100,000 in the bank, more skills than the norm, and a greater chance of creating your life and work from there on out, rather than conforming to make a paycheck. Policy #5: As part of the high school 50/50 system, ensure each student learns the adult financial red tape in your state/country before you’ve got bills, kids, etc.](https://image.24vids.com/tw-2037992019205972418/amplify_video_thumb/2037991926801334272/img/FG1KfaQNnFbWMfhI.jpg)
![[CLIP] by Hand ✍️ The CLIP (Contrastive Language–Image Pre-training) model, a groundbreaking work by OpenAI, redefines the intersection of computer vision and natural language processing. It is the basis of all the multi-modal foundation models we see today. How does CLIP work? Goal: 🟨 Learn a shared embedding space for text and image [1] Given ↳ A mini batch of 3 text-image pairs ↳ OpenAI used 400 million text-image pairs to train its original CLIP model. Process 1st pair: "big table" [2] 🟪 Text → 2 Vectors (3D) ↳ Look up word embedding vectors using word2vec. [3] 🟩 Image → 2 Vectors (4D) ↳ Divide the image into two patches. ↳ Flatten each patch [4] Process other pairs ↳ Repeat [2]-[3] [5] 🟪 Text Encoder & 🟩 Image Encoder ↳ Encode input vectors into feature vectors ↳ Here, both encoders are simple one layer perceptron (linear + ReLU) ↳ In practice, the encoders are usually transformer models. [6] 🟪 🟩 Mean Pooling: 2 → 1 vector ↳ Average 2 feature vectors into a single vector by averaging across the columns ↳ The goal is to have one vector to represent each image or text [7] 🟪 🟩 -> 🟨 Projection ↳ Note that the text and image feature vectors from the encoders have different dimensions (3D vs. 4D). ↳ Use a linear layer to project image and text vectors to a 2D shared embedding space. 🏋️ Contrastive Pre-training 🏋️ [8] Prepare for MatMul ↳ Copy text vectors (T1,T2,T3) ↳ Copy the transpose of image vectors (I1,I2,I3) ↳ They are all in the 2D shared embedding space. [9] 🟦 MatMul ↳ Multiply T and I matrices. ↳ This is equivalent to taking dot product between every pair of image and text vectors. ↳ The purpose is to use dot product to estimate the similarity between a pair of image-text. [10] 🟦 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [11] 🟦 Softmax: ∑ ↳ Sum each row for 🟩 image→🟪 text ↳ Sum each column for 🟪 text→ 🟩 image [12] 🟦 Softmax: 1 / sum ↳ Divide each element by the column sum to obtain a similarity matrix for 🟪 text→🟩 image ↳ Divide each element by the row sum to obtain a similarity matrix for 🟩 image→🟪 text [13] 🟥 Loss Gradients ↳ The "Targets" for the similarity matrices are Identity Matrices. ↳ Why? If I and T come from the same pair (i=j), we want the highest value, which is 1, and 0 otherwise. ↳ Apply the simple equation of [Similarity - Target] to compute gradients of for both directions. ↳ Why so simple? Because when Softmax and Cross-Entropy Loss are used together, the math magically works out that way. ↳ These gradients kick off the backpropagation process to update weights and biases of the encoders and projection layers (red borders).](https://image.24vids.com/tw-1801370086152016034/ext_tw_video_thumb/1801368762937131008/pu/img/GuRh5lcJ6M7HsrkY.jpg)
