Light Light Gray (Digital+Print) Light Gray (Digital+Print) Medium Gray (Digital+Print) Dark Gray (Digital+Print) #F6F6F6 R246, G246, B246 C00, M00, Y00, K04 Pantone Cool Grey 1 C #FFFFFF as the background color, and use Coding color—Dark Gray, Light Gray, Green, Yellow, and reference other PyTorch Brand colors to use. At the same time, please ensure the clarity and legibility of Green (Digital) Coding Text— Light Gray (Digital) Coding Text— Dark Gray (Digital) Coding Background— Dark (Digital) Coding Background— Light (Digital) Hex #2B7D6D Hex #F4A623

similar to the baseline, but does so in fewer epochs. We could ideally save an epoch’s worth of training time by terminating the training early, if we adopt this hypothetical sample efficient model training. effective utilization of the training data. Labeling data is often an expensive process both in terms of time consumption and fiscal expenditure because it involves human labelers looking at each example and the four classes, three of which are the keywords that the device will accept: hello, weather and time. The fourth class (none) indicates the absence of an acceptable keyword in the input signal. Figure

toronto.edu/~fritz/absps/cvq.pdf probability distributions Meanwhile: Speech Sequence ▪ No Memory ▪ Time delay NN http://www.cs.toronto.edu/~fritz/absps/waibelTDNN.pdf Moving window ▪ Inspired LeCun Vector Machine: 1992 http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf Dark time ▪ Paper got rejected ▪ Hinton moved to CIFAR seeking for funding ▪ Conspiracy: rebrand“neural

is, it relies on the momentum of the weights which is an exponentially smoothed estimate of over time. For instance, the momentum of weight at training step is given by: 2 Dettmers, Tim, and Luke Zettlemoyer scores, but they will all try to approximate the importance of a given weight at a certain point of time in the training process to minimize the loss function. The better we can estimate this importance granularities visually. Figure 5-4: An example of sparsified weight matrices (zero-d weights are dark) each with 33% sparsity at various granularity levels. It shows the parameter layout for a convolutional

算在内 cpu_time = timeit.timeit(cpu_run, number=3) gpu_time = timeit.timeit(gpu_run, number=3) print('warmup:', cpu_time, gpu_time) # 正式计算 10 次，取平均时间 cpu_time = timeit.timeit(cpu_run timeit(cpu_run, number=10) 预览版202112 第 1 章 人工智能绪论 16 gpu_time = timeit.timeit(gpu_run, number=10) print('run time:', cpu_time, gpu_time) 将不同大小?下的 CPU 和 GPU 环境的运算时间绘制为曲线，如图 1.21 所示。可以看 到，在矩阵?和矩阵 Fort Lauderdale, FL, USA, 2011. [3] J. Mizera-Pietraszko 和 P. Pichappan, Lecture Notes in Real-Time Intelligent Systems, Springer International Publishing, 2017.

json │ │ └── vocab.json 随后你需要运行 python server.py 来启动你的网页服务。请点击进入 `http://localhost:7860/?__theme=dark` 然后享受使用 Qwen 的 Web UI 吧！ 1.6.2 下一步 TGW 中包含了许多更多用途，您甚至可以在其中享受角色扮演的乐趣，并使用不同类型的量化模型。您可 以训练诸如 LoRA

tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations for leverages mixed precision arithmetic by using Tensor Cores on NVIDIA V100 GPUs for 1.3x faster training time while maintaining target accuracy. This model script is available on GitHub and NGC. ‣ Tacotron tested against each NGC monthly container release to ensure consistent accuracy and performance over time. ‣ ResNeXt101-32x4d model: This model was introduced in the Aggregated Residual Transformations for

model, input data and the hyperparameter trial set is ready. Let's go ahead and train the model, each time choosing one item from the trial set. Each model is trained for 2000 iterations. At the end of a trial on the hyperparameters for the final training. For large models, this is very expensive in terms of time and resources. Alternatively, we can base the search approach on the budget allocation to cap the 24s] val_accuracy: 0.6313725709915161 Best val_accuracy So Far: 0.7284313440322876 Total elapsed time: 00h 17m 23s Results summary Results in hpo/hyperband Showing 3 best trials Trial summary Hyperparameters:

还可以将包含多个datetime对象的列表传给 index参数，同样能创建具有时间戳索引的 Series对象。 date_list = [datetime(2018, 1, 1), datetime(2018, 1, 15] time_se = pd.Series(np.arange(6), index=date_list) 12 创建时间序列 如果希望DataFrame对象具有时间戳索引， 也可以采用上述方式进行创建。 2, 15)] time_df = pd.DataFrame(data_demo, index=date_list) 13 通过时间戳索引选取子集 最简单的选取子集的方式，是直接使用位置 索引来获取具体的数据。 # 根据位置索引获取数据 time_se[3] 14 通过时间戳索引选取子集 还可以使用datetime构建的日期获取其对应 的数据。 date_time = datetime(2015 datetime(2015, 6, 1) date_se[date_time] 15 通过时间戳索引选取子集 还可以在操作索引时，直接使用一个日期字 符串（符合可以被解析的格式）进行获取。 date_se['20150530'] date_se['2018/01/23'] 16 通过时间戳索引选取子集 如果希望获取某年或某个月的数据，则可以 直接用指定的年份或者月份操作索引。 date_se['2015']

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 of the simplest approaches towards efficiency is compression to reduce data size. For the longest time in the history of computing, scientists have worked tirelessly towards storing and transmitting information Footprint Metrics Quality Metrics ● Model Size ● Inference Latency on Target Device ● Training Time for Convergence ● Peak RAM Consumption ● Accuracy ● Precision ● Recall ● F1 ● AUC Table 2-1: