oxford_flowers102 dataset. In the next section, we will retrain the same model but with a twist! Project: Oxford Flower Classification With Hyperparameter Tuning Recall that in chapter 3, we trained a dropout rate was 0.2. The model reached the top accuracy of 70% after training for 100 epochs. In this project, we will let the HyperBand choose the best values for these hyperparameters and see if we can do alse) ) Let's resize the dataset splits to the same size. The target size is identical to the project in chapter 3. # Dataset image size IMG_SIZE = 264 def resize_image(image, label): image = tf.image

Smaller, Faster, and Better." arXiv preprint arXiv:2106.08962 (2021). It’s time for a hands-on project to apply our recent learnings and measure their impact. We will use the oxford_flowers102 dataset performances with and without data augmentation to measure the benefits of the techniques we just learnt. Project: Oxford Flowers Classification The oxford_flowers102 dataset contains 1020 labeled examples each good quality model. So, we use a pre-trained ResNet50 model and fine tune it. The code for this project is available as a Jupyter notebook here. Tensorflow provides easy access to this dataset through

pruning technique because of its simplicity and effectiveness. Later on in this chapter, we have a project that relies on it for sparsifying a deep learning model. The authors of the Optimal Brain Damage trained a model to predict masks for pets to build snapchat like filters. Let’s continue on the same project to demonstrate how we can create a pruned network without significant drop in accuracy in the next 09723v1 3 https://github.com/google/XNNPACK Project: Lightweight model for pet filters application Recall that our regular CNN model in the pet filters project consisted of thirteen convolution blocks and

your embeddings similar to Word2Vec, GloVe, and other embedding methods. How about we jump into a project now to demonstrate how embeddings can be used to achieve a high performing model while optimizing the recent literature. Using pre-trained embeddings to improve accuracy of a NLP task. In this project we will work with the DBPedia dataset, which has snippets of text from Wikipedia. Our goal is to attention parameters, the next component to attack is the softmax computation. The Low Rank methods project the keys and the values to a smaller dimension k to reduce the computation and memory complexity

such models, the high costs of pre-training get spread over the number of applications using it. Project: Using Pre-trained Language Models for News Classification That was a lot of talk without any code we need, and the number of training epochs we need to achieve our desired model quality. In this project we will demonstrate that self-supervised models provide both those efficiency gains. We will work quality and faster convergence than a BERT model that is trained from scratch. The code for this project is available here as a Jupyter notebook. We will not be explicitly demonstrating pre-training BERT

quantization section delves into the implementation details using code samples. We finish with a hands-on project that will walk you through the process of applying quantization in practical situations using popular precision trade off! It’s time to put our understanding of quantization into practice with a hands-on project. We will apply the learnings from weight and activation quantizations to a real world deep learning learning model and demonstrate the size reduction and inference efficiency improvements. The project will use the famous MNIST dataset! Figure 2-10: Latency v/s accuracy trade off for unoptimized representation

about PyTorch, including tutorials, documentation, and examples, see: ‣ PyTorch website ‣ PyTorch project This document provides information about the key features, software enhancements and improvements Facebook's Fairseq NLP Toolkit and is built on top of PyTorch. The original version in the Fairseq project was developed using Tensor Cores, which provides significant training speedup. Our implementation The original version PyTorch Release 19.12 PyTorch RN-08516-001_v23.07 | 282 in the Fairseq project was developed using Tensor Cores, which provides significant training speedup. Our implementation

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 -m venv kerasenv Keras 7 Quit virtual environment After finishing all your changes in your project, then simply run the below command to quit the environment: deactivate Anaconda Cloud We believe

• 在测试集上评估最佳模型 • 解释模型结果 • 得出结论 25 参考文献 1. https://github.com/WillKoehrsen/machine-learning-project- walkthrough 26 谢 谢！

Vision Computing, 30:65-77, 2012. Visual-Inertial SLAM • 使用IMU数据提高鲁棒性 • 基于滤波的方法 • MSCKF, SLAM in Project Tango, … • 基于非线性优化的方法 • OKVIS, … • 没有真实IMU数据的情况下，是否能够通过视觉的方法 来模拟IMU数据? RKSLAM • 基于多单应矩阵的跟踪