complex relationships. Convolutional Neural Nets (CNNs) were another important breakthrough that enabled learning spatial features in the input. Recurrent Neural Nets (RNNs) facilitated learning from the sequences efficient layers and architectures. Let’s start our journey with learning about embeddings in the next section. Embeddings for Smaller and Faster Models We humans can intuitively grasp similarities between part of modern deep-learning models, and we are excited to explain how they work. In the following section we will explain them through a toy example, but feel free to jump ahead if you are familiar with

are and how to employ them in deep learning workflows. We start with data augmentation in the next section. Data Augmentation Data Augmentation is a set of dataset manipulation techniques to improve sample # Shift towards high tones transform_and_show(image_path, channel_shift_intensity=100) In this section, we discussed a number of spatial and value transformation techniques for image data. We used an the best weights for later use! That’s it for the label invariant image transformations! In this section, we presented various image transformation techniques that can be used to augment an image dataset

ca/~vincentp/ift3395/lectures/backprop_old.pdf GAN:2014 https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf BigGAN https://arxiv.org/pdf/1809.11096.pdf Ian Goodfellow ▪ How I fail https://veronikach

软件，可以同时获得 Python 解释器、包管理和虚拟环境等一系列 便捷功能，何乐而不为呢。首先从 https://www.anaconda.com/distribution/#download-section 网址进入 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 参考文献 [1] G. E. Hinton, S. Osindero 和 Y.-W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,” Neural Comput., 卷 18, pp. 1527-1554, 7 2006. [2] Y. LeCun, B. Boser, J. S. Denker, D. Henderson Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville 和 Y. Bengio, “Generative Adversarial Nets,” 出处 Advances in Neural Information Processing Systems 27, Z. Ghahramani, M. Welling, C. Cortes,

J., Mirza, M., Xu, B., Warde‐Farley, D., Ozair, S., ⋯ Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems (pp. 2672–2680). [Gotmare et al., 2018] Gotmare Hochreiter, S., Bengio, Y., Frasconi, P., Schmidhuber, J., & others (2001). Gradient flow in recurrent nets: the difficulty of learning long-term dependencies. [Hochreiter & Schmidhuber, 1997] Hochreiter,

the model as a blackbox. The set which performs the best is chosen for full training. In the next section, we'll discuss various approaches for hyperparameter optimization. Hyperparameter Optimization promising ones. This is called Configuration Evaluation. Let's discuss it in detail in the next section. Figure 7-3: (a) Bayesian Optimization Search on a two dimensional search space. The red areas chapter 3, we trained a model to classify flowers in the oxford_flowers102 dataset. In the next section, we will retrain the same model but with a twist! Project: Oxford Flower Classification With Hyperparameter

Details of quantization and its applications in deep learning follow right after. The quantization section delves into the implementation details using code samples. We finish with a hands-on project that of a layer or a collection of layers, such that it meets the desired tradeoff goals. In the next section we introduce Quantization, a popular compression technique which is also used in various fields of otherwise be too big to execute on such devices. We will tackle this exact problem in the following section. Figure 2-8: Image sizes with various degrees of quantization Quantization in Deep Learning Models

with a small number of labels. As we described in chapter 3’s ‘Learning Techniques and Efficiency’ section, labeling of training data is an expensive undertaking. Factoring in the costs of training human Gidaris et al.. Once the general model is ready, we can fine-tune it for a specific task. The next section discusses it in detail. Fine Tuning On Labeled Data The next step in using the pre-trained models top of the last hidden layer as demonstrated by Howard et al.. As we mentioned in the previous section, this idea of pre-training followed by fine-tuning is also used in BERT (Devlin et al.), and other

weights as well. Such techniques are classified under structured pruning techniques. In the next section, we will discuss pruning at different granularities. Sparsity Granularities The examples of sparsity demonstrate how we can create a pruned network without significant drop in accuracy in the next section. 4 Elsen, E., Dukhan, M., Gale, T., & Simonyan, K. (2019). Fast Sparse ConvNets. arXiv, 1911.09723 However, we could also use a variable sparsity value across epochs as explained in the earlier section. Active Research Some recent works like Sparse Evolutionary Training5 (SET), Dynamic Sparse Reparametrization6

the following: ‣ Ubuntu 16.04 including Python 3.6 environment ‣ NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 9.0.425 ‣ NVIDIA the following: ‣ Ubuntu 16.04 including Python 3.6 environment ‣ NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 9.0.425 ‣ NVIDIA environment ‣ NVIDIA CUDA 9.0.176 (see Errata section and 2.1) including CUDA ® Basic Linear Algebra Subroutines library ™ (cuBLAS) 9.0.333 (see section 2.3.1) ‣ NVIDIA CUDA ® Deep Neural Network library