context (neighboring words), and the label (masked word to be predicted). The word tokens are vectorized by replacing the actual words by their indices in our vocabulary. If a word doesn’t exist in the overfitting. We can now vectorize the train and test datasets. x_train_vectorized = vectorization_layer(x_train) x_test_vectorized = vectorization_layer(x_test) Step 3: Initialization of the Embedding model! bow_model_w2v_history = bow_model_w2v.fit( x_train_vectorized, y_train, batch_size=64, epochs=10, validation_data=(x_test_vectorized, y_test)) Epoch 1/10 313/313 [==============================]

solution. It supports vector operations which operate on a vector (or a batch) of x variables (vectorized execution) instead of one variable at a time. Although it is possible to work without it, you would deep learning applications which frequently operate on batches of data. Using vectorized operations also speeds up the execution (and this book is about efficiency, after all!). We highly recommend learning

following vectorized form, J(θ) = 1 2m (Xθ − ⃗y)T (Xθ − ⃗y) where ⃗y = � ���� y(1) y(2) ... y(m) � ���� , X = � ���� −(x(1))T − −(x(2))T − ... −(x(m))T − � ���� The vectorized version is

� hθ(x(i)) � 1 − hθ(x(i)) � x(i) � x(i)�T � (8) Note that the formulas presented above are the vectorized versions. Specifically, this means that x(i) ∈ Rn+1, x(i) � x(i)�T ∈ R(n+1)×(n+1), while hθ(x(i))

activation • σ′(z[L] j ) measures how fast the activation function σ is changing at zL j • A vectorized form δ[L] = ▽aL ⊙ σ′(z[L]) 13 / 19 Fundamental Equations • An equation for the error δl in terms

convolution, an intermittent silent failure might happen due to dependency on the order of the stream execution. In some cases this might be manifested as NaNs in the output and we recommend to disable cuDNN convolution, an intermittent silent failure might happen due to dependency on the order of the stream execution. In some cases this might be manifested as NaNs in the output and we recommend to disable cuDNN updates ‣ Initial support for channel-last layout for convolutions ‣ Support for loop unrolling and vectorized loads and stores in TensorIterator ‣ Support for input activations with more than 231 values

keras：分布式和高性能的 Keras • 构建和训练模型的高层次 API • API 完全兼容原生 Keras • 支持保存和加载 TensorFlow SavedModel • 支持 Eager Execution • 支持分布式训练 tf.data：功能强大的数据管理模块 支持多种数据处理 图像解码 Shuffle py_function 重采样 支持多种数据格式 图像文件 文本文件 CSV

(continued) • Which one do you think is better? PyTorch! • Easy Interface − easy to use API. The code execution in this framework is quite easy. Also need a fewer lines to code in comparison. • It is easy to

[split/type conversion] Sequence Feature [side info] Op Fusion [hash + embedding] Overlap Execution [FG OP化] Item Feature增量更新 3.工程优化复 杂 4.数据获取困 难 挑战 深度模型是非线性的: • 参数很多 • 参数敏感 • 不同场景的数据上差异大

results. The trials are independent of each other which makes them a good candidate for parallel execution. For example, the trial set for two hyperparameters and where and is Figure 7-2 (a) shows results