time: • Google Colab provides free Tesla K80 GPU of about 12GB. You can run the session in an interactive Colab Notebook for 12 hours. • https://colab.research.google.com/ Misc • Dynamic VS Static Computation loss loss y_train_tensor Misc • Dynamic VS Static Computation Graph Building the graph and computing the graph happen at the same time. Seems inefficient, especially if we are building the same

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 in as few bits as 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 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

which combined the accuracy and latency metrics. It searched for Pareto optimal child networks by computing their latencies 7 Tan, Mingxing, et al. "Mnasnet: Platform-aware neural architecture search for defines a ChildManager class which is responsible for spawning child networks, training them, and computing rewards. The layers constant defined in the class indicates the stacking order of the cells. Each the second step, the child network is training on the CIFAR-10 dataset. The third step involves computing reward which is the difference between the accuracy and the rolling average of past accuracies over

Singapore. Research Interests: Distributed Algorithms and Systems, Wireless Net- works, Mobile Computing, Internet of Things. Feng Li (SDU) Overview September 6, 2023 3 / 57 Course Information We will 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 (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

Classic AI Modern AI 2016~Now 2010~Now AIIDE IEEE CIG SSCAIT Reinforcement Learning Agent Environment Action Observation Reward Goal Deep Reinforcement Learning What is next? • All above are Overcoming catastrophic forgetting in neural networks Memory-Augmented Neural Networks Source: Hybrid computing using a neural network with dynamic external memory Work Fun Play Hard

and deep learning models. TensorFlow is very flexible and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as Python, C#, 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

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 Docker 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 before you proceed to step 3. 3. To run the container image, select one of the following modes: ‣ Interactive ‣ If you have Docker 19.03 or later, a typical command to launch the container is: docker run

'model_server': '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 bge-small as the embedding model or modify the context window size or text chunk size depending on your computing resources. Qwen 1.5 model families support a maximum of 32K context window size. import torch from

them. Concretely, a practitioner might want to experiment with at least the following aspects: 1. Computing saliency scores. 2. Deciding on a pruning schedule. 3. Unstructured / Structured sparsity. Seems derivative gives us a clearer insight into how important might be to minimize the loss. Since computing pairwise second-derivatives for all and might be very expensive (even with just weights, this proportion to the mean magnitude of momentum of weights in that layer. There might be other ways of computing saliency scores, but they will all try to approximate the importance of a given weight at a certain

observed that, the feature mapping leads to a huge number number of new features, such that i) computing the mapping itself can be inefficient, especially when the new feature space is of much higher dimension; can be expensive (e.g., we have to store all the high-dimensional images of the data samples and computing inner products in the high-dimensional feature space is of considerable overhead). Fortunately, implicitly defines a mapping φ(x) = {x2 1, √ 2x1x2, x2 2} Through the kernel function, when computing the inner product < φ(x), φ(z) >, we do not have to map x and z into the new higher-dimensional