or momentum are geared towards model convergence. However, they all work in conjunction to produce better models faster. Let's say that we are optimizing the validation loss, , for a given dataset on a important hyperparameters. Important hyperparameters have a larger number of subspaces or subranges than unimportant parameters that need to be searched for an optimal value. For example, in the US presidential benefit from a more aggressive focus (search) on the counties in swing states (important parameters) than those in the rest of the states (unimportant parameters). Figure 7-2: A comparison of hyperparameter

advanced compression techniques. By ‘advanced’ we mean that these techniques are slightly more involved than quantization (as discussed in the second chapter). But that doesn’t mean they are harder to learn quantizing is not uniformly distributed, i.e. the data is more likely to take values in a certain range than another equally sized range. It creates equal sized quantization ranges (bins), regardless of the sparsified weight matrix size. As shown in the output below, the sparsified compressed matrix is smaller than the regular compressed matrix by nearly 50%. weights = np.random.normal(size=(100, 100)).astype(np

application that employs a high quality model with a reasonable translation accuracy would garner better consumer support. In this chapter, our focus will be on the techniques that enable us to achieve metrics). We designate a new model training setup to be more sample efficient, if it achieves similar or better performance with fewer data samples when compared to the baseline. Think of it as teaching a child efficient and/or label efficient training setup, can we exchange some of this to achieve a model with a better footprint? The next subsection elaborates it further. Using learning techniques to build smaller

it can be rewarding to go back to the drawing board and experiment with another architecture that better suits the task. As an analogy, when renovating a house to improve the lighting, it is possible to have been domesticated for a while and are safe. These two animals are more similar to each other than to a random animal like a chimp. Similarly, we know that we should maintain our distance from a snake Guardians of the Galaxy?), but it is not the safest animal to be in close proximity, though still safer than a snake or a bear. A domestic mouse on the other hand is not cute but nor very dangerous, but you

Finance L.P. All rights reserved. Performance – Better than Human Precision Recall Machine Human Machine Human Table Boundary 95% 94% 95% 95% Perfect Table 87% 82% 94% 94% • 48,607 pages evaluated

过拟合&欠拟合 主讲人：龙良曲 Scenario1: house price Scenario2: GPA The ground-truth distribution? ▪ That’s perfect if known ▪ However Another factor: noise ▪ ? = ? ∗ ? + ? + ? ▪ ? ∽ ?(0.01, 1) ▪ 1.567 = w * well Case2: Ground-truth < Estimated over- fitting Overfitting ▪ train loss and acc. is much better ▪ test acc. is worse ▪ => Generalization Performance Overfitting ! ▪ how to detect ▪ how to

GPUs. It includes support for 8-bit floating point (FP8) precision on Hopper GPUs which provides better training and inference performance with lower memory utilization. Transformer Engine also includes mixed precision Tensor Cores on NVIDIA Volta and NVIDIA Turing™, so you can get results much faster than training without Tensor Cores. PyTorch Release 23.07 PyTorch RN-08516-001_v23.07 | 10 This relative positional encoding. The enhancements that were introduced in Transformer-XL help capture better long-term dependencies by attending to tokens from multiple previous segments. Our implementation

without taking a hit on any of the footprint metrics. These techniques might get superseded by other better methods over time, but again our goal is to give you a gentle introduction to this area for you to techniques like data-augmentation, distillation etc. as introduced in chapter 3 do help us achieve better quality with fewer labels and fewer training steps required for convergence, they do not alleviate compute-efficient. Pre-training + Fine-tuning helps the models converge faster, attain similar or better quality for the same amount of labeled data when compared to training from scratch, etc. ULMFiT

(2012): 1097-1105. do linear algebra operations such as multiplying two matrices together much faster than traditional CPUs. Advances in the training algorithms There has been substantial progress in machine matching it to the given guidelines. The ImageNet dataset was a big boon in this aspect. It has more than 1 million labeled images, where each image belongs to 1 out of 1000 possible classes. This helped If we have two models performing equally well on a given task, we would choose the one which does better on training or inference efficiency metrics, or both (depending on the use case). For example, if

Chapter 2 - Compression Techniques “I have made this longer than usual because I have not had time to make it shorter.” Blaise Pascal In the last chapter, we discussed a few ideas to improve the deep interested in compressing in a lossless or lossy manner. We can fit 10 apples in a smaller box with a better arrangement. This is lossless compression. Another approach is to chop them into cubes and discard frequent symbols will take the least number of bits to represent. In aggregate, this would be better than encoding each symbol with the same number of bits. The lookup table (figure 2-1 middle) that contains