(and advantages) • Preview of Numpy & PyTorch & Tensorflow Numpy Tensorflow PyTorch Computation Graph Advantages (continued) • Which one do you think is better? Advantages (continued) • Which one • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various other functions • loss (MSE,CE etc..) • optimizers Prepare Input Data •Load data •Iterate to run on. Visualization • TensorboardX (visualise training) • PyTorchViz (visualise computation graph) https://github.com/lanpa/tensorboardX/ Visualization (continued) • PyTorchViz https://github

分布式存储  分布式查询 功能完备:  GSL/负采样  主流图算法  异构图 (user/item/attribute)  动态图 标准化: Standard Libraries Graph-Learn: 分布式图算法库 标准化: Standard Solutions Continuous Optimization: Active learning Data Label Model [VariationalDropout] 通信优化 [GRPC++] 实时训练 [增量更新] 混合精度 [bf16] 工程优化: 千亿特征优化 模型蒸馏 AVX/SSE优化 Graph优化 [User Graph去重] 内存Allocate优化 ParallelStringOp [split/type conversion] Sequence Feature [side info] com/alibaba/EasyCV 6. EasyNLP: https://github.com/alibaba/EasyNLP 7. AliGraph: https://github.com/alibaba/graph-learn 8. DSW: https://help.aliyun.com/document_detail/194831.html 9. DLC: https://help.aliyun.c

values of θ0 and θ1 that you get, and plot the straight line fit from your algorithm on the same graph as your training data according to θ. The plotting commands will look something like this: hold on figure below. 0 10 20 30 40 50 Number of iterations 0 1 2 3 4 5 6 7 Cost J 1010 If your graph looks very different, especially if your value of J(θ) increases or even blows up, adjust your learning learning rates affect convergence, it’s helpful to plot J for several learning rates on the same graph. In Matlab/Octave, this can be done by performing gradient descent multiple times with a hold on command

Adjustment） • 变量数目非常庞大 • 内存空间需求大 • 计算耗时 • 迭代的局部集束调整 • 大误差难以均匀扩散到整个序列 • 极易陷入局部最优 • 姿态图优化（Pose Graph Optimization） • 只优化相机之间的相对姿态，三维点都消元掉； • 是集束调整的一个近似，不是最优解。 基于自适应分段的集束调整 • 将长序列分成若干段短序列； • 每个短 Recognition Pose Graph Optimization + Traditional BA Street序列结果比较 ENFT-SLAM ORB-SLAM Non-consecutive Track Matching Segment-based BA Bag-of-words Place Recognition Pose Graph Optimization + Traditional

interesting aspects of the data Examples: Discovering clusters Discovering latent factor Discovering graph structure Matrix completion Feng Li (SDU) Overview September 6, 2023 28 / 57 Unsupervised Learning: Discovering Graph Structures Sometimes we measure a set of correlated variables, and we would like to discover which ones are most correlated with which others This can be represented by a graph, in which

MLX平台架构 • 基于Worker + PS架构搭建 • Worker  模型计算引擎（Engine）  计算图框架（Graph） • 模型计算引擎Engine  模型结构处理  与PS通信交换模型参数  计算图的计算 • 计算图框架Graph  计算逻辑抽象op，通过op组合形成模型结构  提供正向（forward）、反向（backward）、Loss的操作扩展

非 叶 子 节 点 梯 度 ， 需 使 对 应 张 量 调 用 retain_graph （） 使 用 t e n s o r.grad.zero_() 清 除 张 量 梯 度 如 果 要 保 留 计 算 图 ， 可 通 过 设 置 b a c kw a r d（ ） 中 参 数 retain_graph=True 释 放 计 算 图 具体实例可参考书中2.7小节内容

Figure 1-9: Illustration of the pruning process. On the left is the unpruned graph, and on the right is a pruned graph with the unimportant connections and neurons removed. Learning Techniques Learning

tensorflow.org/api_docs/python/tf/set_random_seed tf.set_random_seed(1234) sess = tf.Session(graph=tf.get_default_graph(), config=session_conf) K.set_session(sess) # 剩余代码 ... 3.3.21 如何在 Keras 中安装 HDF5 或 h5py TensorBoard [source] keras.callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True, write_grads=False, write_images=False, embeddings_freq=0, embeddings_layer_names=None, em 对于模型中各个层计算激活值和模型权重直方图的频率（训练轮数中）。 如果设置成 0 ，直方图不会被计算。对于直方图可视化的验证数据（或分离数据）一定要 明确的指出。 • write_graph: 是否在 TensorBoard 中可视化图像。如果 write_graph 被设置为 True，日志文 件会变得非常大。 • write_grads: 是否在 TensorBoard 中可视化梯度值直方图。histogram_freq

quality image. b=2 b=4 b=5 b=6 b=7 b=8 Figure 2-7: Images with various degrees of quantization. A graph of quantized representation bit size (b) and the resulting image sizes (in bits) is shown in figure model. Now, let’s get it ready for training. The get_compiled_model() function creates our model graph using the create_model() function. Then, it compiles the model by providing the necessary components