quite easy. Follow below steps to properly install Keras on your system. Step 1: Create virtual environment Virtualenv is used to manage Python packages for different projects. This will be helpful to a virtual environment while developing Python applications. Linux/Mac OS Linux or mac OS users, go to your project root directory and type the below command to create virtual environment, python3 keras Step 2: Activate the environment This step will configure python and pip executables in your shell path. Linux/Mac OS Now we have created a virtual environment named “kerasvenv”. Move to the

Before you begin Before you can run an NGC deep learning framework container, your Docker ® environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in Running container (defaults to all GPUs, but can be specified by using the NVIDIA_VISIBLE_DEVICES environment variable). For more information, refer to the nvidia-docker documentation. Note: Starting in Docker of the version of PyTorch in /opt/ pytorch. It is prebuilt and installed in the default Python environment (/usr/local/lib/ python3.10/dist-packages/torch) in the container image. The container also includes

approximately the same . Such a model is useful if we want to deploy a model in a space constrained environment like a mobile device. To summarize, compression techniques help to achieve an efficient representation Quantization of sine waves. Let’s dig deeper into its mechanics using an example. Let’s assume we have a variable x which takes a 32-bit floating point value in the range [-10.0, 10.0]. We need to transmit a collection the repository in the form of Jupyter notebooks. You can run the notebooks in Google’s Colab environment which provides free access to CPU, GPU, and TPU resources. You can also run this locally on your

unlabeled example in the environment Learner can construct an arbitrary example and query an oracle for its label Learner can design and run experiments directly in the environment without any human guidance for which its value is known The thing we want to predict is called the target or the response variable Usually, we need training data Feng Li (SDU) Overview September 6, 2023 23 / 57 Supervised Learning (SDU) Overview September 6, 2023 33 / 57 Reinforcement Learning Learning from interaction (with environment) Goal-directed learning Learning what to do and its effect Trial-and-error search and delayed

because they do not model the concept of order well. Another limitation of HPO is the search for variable length architectures. HPO requires all the hyperparameters to be known prior to the start of the performance on the image and language benchmark datasets. Moreover, their NAS model could generate variable depth child networks. Figure 7-4 shows a sketch of their search procedure. It involves a controller as input to generate the next action and so on. We can design a recurrent model with a fixed or a variable number of time steps. Figure 7-5 shows a general architecture of the NAS recurrent model. The time

= tf.placeholder(tf.float32, name='variable_a') b_ph = tf.placeholder(tf.float32, name='variable_b') # 创建输出端子的运算操作，并命名 c_op = tf.add(a_ph, b_ph, name='variable_c') 创建计算图的过程就类比通过符号建立公式? = ? + ?的过程，仅仅是记录了公式的计算步 Anaconda 下载页面，选择 Python 最新版本的下载链接即可下载，下载完成后安 装即可进入安装程序。如图 1.22 所示，勾选”Add Anaconda to my PATH environment variable”一项，这样可以通过命令行方式调用 Anaconda 程序。如图 1.23 所示，安装程序 询问是否连带安装 VS Code 软件，选择 Skip 即可。整个安装流程约持续 5 分钟，具体时间 Layer 基类。创建初始化方法，并调用母类的初始化函数，由 于是全连接层，因此需要设置两个参数：输入特征的长度 inp_dim 和输出特征的长度 outp_dim，并通过 self.add_variable(name, shape)创建 shape 大小，名字为 name 的张量?， 并设置为需要优化。代码如下： 预览版202112 8.4 自定义网络 9 class MyDense(layers

'dashscope', # 'api_key': 'YOUR_DASHSCOPE_API_KEY', # It will use the `DASHSCOPE_API_KEY' environment variable if 'api_key' is not␣ �→set here. # Use your own model service compatible with OpenAI API: input_variables=["context_str", "question"] ) chain_type_kwargs = {"prompt": prompt, "document_variable_name": "context_str"} qa = RetrievalQA.from_chain_type( llm=llm, chain_type=CHAIN_TYPE, retriever=docsearch

GDA, NB and EM September 27, 2023 6 / 122 Conditional Probability (Contd.) Real valued random variable is a function of the outcome of a ran- domized experiment X : S → R Examples: Discrete random variables valued random variable is a function of the outcome of a ran- domized experiment X : S → R For continuous random variable X P(a < X < b) = P({s ∈ S : a < X(s) < b}) For discrete random variable X P(X = Distribution Probability distribution for discrete random variables Suppose X is a discrete random variable X : S → A Probability mass function (PMF) of X: the probability of X = x pX(x) = P(X = x) Since

super(MyLayer, self).__init__(**kwargs) def build(self, input_shape): # Create a trainable weight variable for this layer. self.kernel = self.add_weight(name='kernel', shape=(input_shape[1], self.output_dim) inputs = K.placeholder(ndim=3) 下面的代码实例化一个变量。它等价于 tf.Variable() 或 th.shared()。 import numpy as np val = np.random.random((3, 4, 5)) var = K.variable(value=val) # 全 0 变量： var = K.zeros(shape=(3, 4, 5)) 使用随机数初始化张量 b = K.random_uniform_variable(shape=(3, 4), low=0, high=1) # 均匀分布 c = K.random_normal_variable(shape=(3, 4), mean=0, scale=1) # 高斯分布 d = K.random_normal_variable(shape=(3, 4), mean=0, scale=1)

a sample 2D weight matrix with randomly initialized float values. We also define a sparsity_rate variable initialized with the value 0.4 to sparsify 40% of the total number of weights. Finally, we compute that you are convinced that sparsity helps with improving compression. Increasing the sparsity_rate variable’s value will further reduce the size of the sparsified and compressed size. To take a step back 5-2 uses a fixed pruning rate $$p$$. However, we could use variable pruning rates across the pruning rounds. The motivation behind using variable sparsity is that a pre-trained model’s weights will get disrupted