basics of deep learning, Keras models, Keras layers, Keras modules and finally conclude with some real-time applications. Audience This tutorial is prepared for professionals who are aspiring to make .......................................................................................... 61 Compile the model ...................................................................................... ................................................................................ 77 14. Keras ― Time Series Prediction using LSTM RNN ................................................................

more that you read, the more things you will know. The more that you learn, the more places you'll go.” ― Dr. Seuss Model quality is an important benchmark to evaluate the performance of a deep learning 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

4.2.3 Sequential 模型方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.3.1 compile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.3.2 fit . . . . . . . 49 4.3.3 Model 类模型方法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.3.1 compile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.3.2 fit . . . . . . . input_dim=100)) model.add(Dense(units=10, activation='softmax')) 在完成了模型的构建后, 可以使用 .compile() 来配置学习过程： model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) 如果需要，你还可以进一步地配置你的优化器。Keras

However, owing to their incremental nature, they offer limited gains. Sometimes, it can be rewarding to go back to the drawing board and experiment with another architecture that better suits the task. As an reduction. We will explain these techniques in further detail in chapter 6. A Petting Zoo for Kids Let’s go back to our example of cute and dangerous animals, and represent each animal using two features, say there a way to automate the embedding table generation? Turns out there is! In the next section, let's go over a real world example of embedding table generation by leveraging deep learning to do the grunge

activation='softmax') ]) Our 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. 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 halving, this is set to 2. For HyperBand, the recommended factor is 3. We will use the same. Now, let's go on and load the required modules and the dataset. import tensorflow as tf import tensorflow_datasets

weights will have been removed. Now that we have presented a general algorithm for pruning, we should go over some examples of different ways we implement them. Concretely, a practitioner might want to experiment 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

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 be able to research labeled examples over a reasonable number of training epochs to do well on the given task. We will go into details of how this works shortly. For now, let's assume that we have such a general model that compute efficiency since only have to train the model on a small number of examples, saving training time compute too. A Typical Self-Supervised Learning Recipe We can break-down common self-supervised

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:

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.

on Nexus 4 Fit the core library with all dependencies into a single C++ source file Easy to compile on … Amalgamation Runs in browser with Javascript The first image for search learning: Observe environment  Take Action  Achieve Reward  Repeat. Goal is to maximize rewards over time. • There are three interfaces: • getInitState() for initialization • getAction() • setPerception(nextObservation Firewall User Input 应用案例: Capital One “A highly scalable solution, it also offers potential to speed time to market for a new generation of voice and text interactions such as our recently launched Capital