benchmarks higher. Figure 7-1 shows some of the choices we face when working on a deep learning problem in the vision domain for instance. Some of these choices are boolean, others have discrete parameters parameters and still there are the ones with continuous parameters. Some choices even have multiple parameters. For example, horizontal flip is a boolean choice, rotation requires a fixed angle or a range of rotation rotation, and random augment requires multiple parameters. Figure 7-1: The plethora of choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters

and cat, but we know that they are both cute, 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 reduction1. b) The low-dimensional representation should allow us to compute the distance between any two inputs, which is a measure of their similarity. c) Similar inputs should have a small distance, and and dangerous animals, and represent each animal using two features, say cute and dangerous. We can assign values between 0.0 and 1.0 to these two features for different animals. The higher the value, the

like accuracy, precision, recall, etc. which often are our primary quality concerns. We have chosen two of them, namely data augmentation and distillation, to discuss in this chapter. This is because, firstly chapter. We start this chapter with an introduction to sample efficiency and label efficiency, the two criteria that we have picked to benchmark learning techniques. It is followed by a short discussion talking about them in the same breadth as efficiency? To answer this question, let’s break down the two prominent ways to benchmark the model in the training phase namely sample efficiency and label efficiency

multiplying two matrices together much faster than traditional CPUs. Advances in the training algorithms There has been substantial progress in machine learning algorithms over the past two decades. Stochastic rate-limited by their efficiency. While efficiency can be an overloaded term, let us investigate two primary aspects: Training Efficiency Training Efficiency involves benchmarking the model training for examples. Figure 1-3: Some examples of training and iInference efficiency metrics. If we have two models performing equally well on a given task, we would choose the one which does better on training

• It can be extended to recurrent neural networks (RNN) by involving feedback connections, which power many natural language applications 2 / 19 Neuron 3 / 19 Neuron (Contd.) • Neuron activated when (Contd.) • An example: logistic regression function g(x) = 1 1 + exp(−wT x − b) • Break it into two computations • z = wT x + b • a = σ(z) where σ(z) = 1/(1 + e−z) 5 / 19 Neural Feedforward Networks written as a function of the output from the neural network • Hadamard product: Elementwise product of two vectors s ⊙ t such that (s ⊙ t)j = sjtj �1 2 � ⊙ �3 4 � = �1 ∗ 3 2 ∗ 4 � = �3 8 � 12 / 19 Fundamental

knowledge of NumPy will also be useful! What is PyTorch? ● An machine learning framework in Python. ● Two main features: ○ N-dimensional Tensor computation (like NumPy) on GPUs ○ Automatic differentiation Subtraction z = x - y ● Power y = x.pow(2) Common arithmetic functions are supported, such as: Tensors – Common Operations Tensors – Common Operations ● Transpose: transpose two specified dimensions

"model": "Qwen/Qwen1.5-72B-Chat", "prompt": "My favorite food is", "max_tokens": 512 }' | jq -r '.choices[0].text' 3. 向该 endpoint 发送 chat 请求 curl -L http://$IP:8000/v1/chat/completions \ -H "Content-Type: }, { "role": "user", "content": "What is the best food?" } ], "max_tokens": 512 }' | jq -r '.choices[0].message.content' 1.11.4 使用 SkyPilot Serve 扩展服务规模 1. 使用 SkyPilot Serve 扩展 Qwen 的服务规模非常容易，只需运行： me the python code for quick sorting a list of integers." } ], "max_tokens": 512 }' | jq -r '.choices[0].message.content' 1.11.5 使用 Chat GUI 调用 Qwen1.5 可以通过 FastChat 来使用 GUI 调用 Qwen1.5 的服务： 1. 开启一个

(NGC). ‣ Tacotron 2 and WaveGlow v1.1 model. This text-to-speech (TTS) system is a combination of two neural network models: a modified Tacotron 2 model from the Natural TTS Synthesis by Conditioning WaveNet (NGC). ‣ Tacotron 2 and WaveGlow v1.1 model. This text-to-speech (TTS) system is a combination of two neural network models: a modified Tacotron 2 model from the Natural TTS Synthesis by Conditioning WaveNet (NGC). ‣ Tacotron 2 and WaveGlow v1.1 model. This text-to-speech (TTS) system is a combination of two neural network models: a modified Tacotron 2 model from the Natural TTS Synthesis by Conditioning WaveNet

Dxf(x), (2.4.2) 其中符号 d dx和D是微分运算符，表示微分操作。我们可以使用以下规则来对常见函数求微分： • DC = 0（C是一个常数） • Dxn = nxn−1（幂律（power rule），n是任意实数） • Dex = ex • D ln(x) = 1/x 为了微分一个由一些常见函数组成的函数，下面的一些法则方便使用。假设函数f和g都是可微的，C是一个 常数，则： features = np.random.normal(size=(n_train + n_test, 1)) np.random.shuffle(features) poly_features = np.power(features, np.arange(max_degree).reshape(1, -1)) for i in range(max_degree): poly_features[:, i] 能力， 使人类的大脑能够更明智地分配资源来生存、成长和社交，例如发现天敌、找寻食物和伴侣。 10.1.1 生物学中的注意力提示 注意力是如何应用于视觉世界中的呢？这要从当今十分普及的双组件（two‐component）的框架开始讲起： 这个框架的出现可以追溯到19世纪90年代的威廉·詹姆斯，他被认为是“美国心理学之父”(James, 2007)。在 这个框架中，受试者基于非自主性提示和自主性提示有选择地引导注意力的焦点。

Round basic matmul ▪ Torch.mm ▪ only for 2d ▪ Torch.matmul ▪ @ An example >2d tensor matmul? Power Exp log Approximation ▪ .floor() .ceil() ▪ .round() ▪ .trunc() .frac() clamp ▪ gradient clipping