Driving on four-lane highways using vision sensors P: Average distance traveled before a human-judged error E: A sequence of images and steering commands recorded while ob- serving a human driver Feng Li interaction (with environment) Goal-directed learning Learning what to do and its effect Trial-and-error search and delayed reward Feng Li (SDU) Overview September 6, 2023 34 / 57 Reinforcement Learning potential for bias. Feng Li (SDU) Overview September 6, 2023 50 / 57 The Curse of Dimensionality Handling complexity Involve many variables, how can we handle this complexity without get- ting into trouble

PyTorch RN-08516-001_v23.07 | 12 ‣ CVE-2022-45198 - Pillow before 9.2.0 performs Improper Handling of Highly Compressed GIF Data (Data Amplification). ‣ CVE-2022-45199 - Pillow before 9.3.0 allows corresponding libraries in our release: ‣ CVE-2022-45198 - Pillow before 9.2.0 performs Improper Handling of Highly Compressed GIF Data (Data Amplification). ‣ CVE-2022-45199 - Pillow before 9.3.0 allows including gradient AllReduce, Optimizer, and Parameter AllGather operations could fail with a CUDA error. We recommend reducing the scope of the CUDA graph capture as a workaround. PyTorch RN-08516-001_v23

Being able to use as little data for training is critical when the user-data might be sensitive to handling / subject to various restrictions such as the General Data Protection Regulation (GDPR) law6 in

number of precision bits to all subranges of the same length. In this scenario, the dequantization error would be large for ranges where the data is densely distributed. Quantization-aware training can mitigate losses by making the network resilient to the errors, but if we want to minimize the dequantization error, this is not an ideal situation. Refer to figure 5-6 for an illustration of this problem. 13 Kurtz elements. For a moment, let’s assume that the centroids we obtain are optimal, i.e. the reconstruction error when we decode the encoded representation is minimal14. In such a scenario, we can list all the centroids

all the modules, it will add everything and if anything went wrong, you will get module not found error message. Keras 9 This chapter explains Keras backend implementations TensorFlow represent the data format.  epsilon represents numeric constant. It is used to avoid DivideByZero error.  floatx represent the default data type float32. You can also change it to float16 or float64 function and Optimizer are used in learning phase to find the error (deviation from actual output) and do optimization so that the error will be minimized.  Fit the model: The actual learning

4 � = �3 8 � 12 / 19 Fundamental Equations • The error in the j-th neuron in the l-th layer: δ[l] j = ∂L ∂z[l] j • An equation for the error in the output layer δ[L] j = ∂L ∂a[L] j σ′(z[L] j ) Fundamental Equations • An equation for the error δl in terms of the error in the next layer δl+1 δ[l] = ((w[l+1])T δ[l+1]) ⊙ σ′(z[l]) • (w[l+1])T δ[l+1] is to move the error backward through the networks, giving of measure of the error at the output of the l-th layer • ((w[l+1])T δ[l+1]) ⊙ σ′(z[l]) is to move the error backward through the activation function in the layer l, giving us the error δl in the weight

authors report needing fewer labeled examples. Refer to figure 6-6 for a comparison between the error obtained by training from scratch v/s using pre-training strategies. 5 WikiText-103 dataset is derived Text Classification." arXiv, 18 Jan. 2018, doi:10.48550/arXiv.1801.06146. Figure 6-6: Validation error w.r.t. number of training examples for different training methods on IMDb (from scratch, ULMFiT supervised: would be easier to learn. The authors report a statistically significant difference between the final error in the word prediction curriculum and non-curriculum strategies. How do we go about implementing

. . . . . . . . . . . . . 134 7.2.1 mean_squared_error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.2 mean_absolute_error . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.3 mean_absolute_percentage_error . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.4 mean_squared_logarithmic_error . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.5 损失函数（或称目标函数、优化评分函数）是编译模型时所需的两个参数之一： model.compile(loss='mean_squared_error', optimizer='sgd') from keras import losses model.compile(loss=losses.mean_squared_error, optimizer='sgd') 你可以传递一个现有的损失函数名，或者一个 TensorFlow/Theano

and thus save some costs? What if it did not matter to us if x was stored/transmitted with some error (-5.023 v/s -5.0)? If we can tolerate some loss of precision, can we use b-bits and save some space dequantized array upon running the code. Note that the last element was supposed to be 10.0, and the error is 2.5. array([-10. , -7.5, -5. , -2.5, 0. , 2.5, 5. , 7.5, 7.5]) So far so good! We the two weights using the Root Mean Square Error (RMSE), which gives an idea of the amount of information lost in the quantization process. As we see, the error is quite small. weights_dequantized = de

the training data as closely as possible. To measure the error between h✓ and the training data, we define a cost function (also called error function) J(✓) : Rn+1 ! R as follows J(✓) = 1 2 m X i=1 optimization method. In each iteration, the parameters are updated according to the gra- dient of the error (i.e., the cost function) with respect to one training sample only. Hence, it entails very limited