... ml-road / resources / Hands On Machine Learning with Scikit Learn and TensorFlow.pdf Go to file Go to file T; Go to line L; Copy path Find helpful customer reviews and review ratings for Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems at Amazon.com. The held-out set is called validation set (or development set, or dev set). This is an iterative process: get a prototype up and running, analyze its output, come back to this exploration step, Instead of doing this manually, you should write functions for this purpose: reproductibility, reuse in your live system, quickly try various transformations to see which combination works best, WARNING: if you choose to fill the missing values, save the value you computed to fill the training set. The harmonic mean gives much more weight to low values, so the classifier will only get a high F1 score if both recall and precision are high, F1 = 2(precisionrecall)/(precision+recall), The F1 score favors classifiers that have similar precision and recall, Precision/recall trade-off: increasing precision reduces recall, and vice versa. One way to solve this is to shorten the input sequences, for example using 1D convolutional layers, A 1D convolutional layer slides several kernels across a sequence, producing a 1D feature map per kernel. Isn’t that cheating? After the stateful model is trained, it will only be possible to use it to make predictions for batches of the same size as were used during training. Simple ANN architecture. The layers close to the input layer are usually called the lower layers, and the ones close to the outputs are usually called the upper layers. learning with scikit 2nd edition filecr. You also need to write monitoring code to check your system’s live performance at regular intervals and trigger alerts when it drops. But after a while, the learning rate will be too large, so the loss will shoot back up: the optimal learning rate will be a bit lower than the point at which the loss starts to climb (typically about 10 times lower than the turning point), Benefit of using large batch sizes -> GPUs can process them efficiently -> use the largest batch size that can fit in GPU RAM, Try to use a large batch size, using learning rate warmup. This might make the algorithm diverge, with larger and larger values, failing to find a good solution. Decision Trees generally are approximately balanced, so traversing the Decision Tree requires going through roughly O (log 2 ( m )) nodes. How does the company expect to use and benefit from this model? Forecasting: often useful to have some error bars along with your predictions. Since the same parameters W and b are used at each time step, backpropagation will do the right thing and sum over all time steps, Time series: the input features are generally represented as 3D arrays of shape [batch size, time steps, dimensionallity], where dimensionallity is 1 for univariate time series and more for multivariate time series, Trend and Seasonality: when using RNNs, it is generally not necessary to remove trend/seasonality before fitting, but it may improve performance in some cases, since the model will not have to learn the trend and the seasonality. Then you can deploy your model to your production environment. If all classifiers are able to estimate class probabilities (i.e., they all have a predict_proba() method), then you can tell Scikit-Learn to predict the class with the highest class probability, averaged over all the individual classifiers. The model performs well on the training data, but it does not generalize well. (“num”, num_pipeline, num_attribs), ), Training Sparse Models: to achieve fast model at runtime with less memory. If training is unstable or bad performance -> try using a small batch size instead, ReLU activation function is a good default. X = imputer.transform(housing_num), housing_num_tr = pd.DataFrame(X, e.g. Download Free PDF. Decision Trees are intuitive, and their decisions are easy to interpret. This is called Stochastic Gradient Boosting . Contribute to yanshengjia/ml-road development by creating an account on GitHub. This process can be automated, but the training process can take many hours/resources. Hands on Machine Learning with Scikit … Some algorithms are not capable of handling multiple classes natively (e.g., Logistic Regression, SVM). Consequence: Gradient Descent is guaranteed to approach arbitrarily close the global minimum (if you wait long enough and if the learning rate is not too high). For RandomForestClassifier for example, the method to use is .predict_proba(), which returns an array conatining a row per instance and a column per class, each containing the probability that the given instance belongs to the given class. The main problem with Batch Gradient Descent is that it uses the whole training set to compute the gradients at every step -> very slow when training set is large, Stochastic Gradient Descent picks a random instance in the training set at every step and computes the gradients based only on that single instance -> algorithm much faster because it has very little data to manipulate at every iteration. Random Forests can limit this instability by averaging predictions over many trees. dimensionality reduction), Creating new features by gathering new data, Simplify the model (fewer parameters), reduce number of attributes, constrain the model (regularization), Reduce noise (e.g. If your model is underfitting the training data, adding more training examples will not help. The mean squared distance between the original data and the reconstructed data is called the reconstruction error, Kernel trick -> math technique that implicity maps instances into a very high-dimensional space (feature space). Note that the regularization term should only be added to the cost function during training. Browse the TF Hub repository -> copy the code example into your project -> module will be downloaded, along with its pretrained weights, and included in your model, Warning: Not all TF Hub modules support TensorFlow 2 -> check before, Normally -> only looks at past and present inputs before generating its outputs -> it’s “causal” (cannot look into the future) -> makes sense for forecasting time series, For many NLP tasks, often is preferable to look ahead at the next words -> bidirectional recurrent layer (keras.layers.Bidirectional), Keeps track of a short list of the k most promising sentences, and at each decoder step it tries to extend them by one word, keeping only the k most likely sentences. To avoid this restriction, create an identical stateless model, and copy the stateful model’s weights to this model. Sampling features results in even more predictor diversity, trading a bit more bias for a lower variance. Michael J. J. Douglass 1,2 All you need to do is replace voting=”hard” with voting=”soft” and ensure that all classifiers can estimate class probabilities. Otherwise, use the ROC curve. If the performance is still not great, then try tuning model hyperparameters such as the number of layers, the number of neurons per layer, and the types of activation functions to use for each hidden layer. After a model is created, you must call its compile() method to specify the loss function and the optimizer to use. Nonparametric model, not because it does not have any parameters but because the number of parameters is not determined prior to training, so the model structure is free to stick closely to the data. ]), housing_prepared = full_pipeline.fit_transform(housing). k = beam width, Allow the decoder to focus on the appropriate words (as encoded by the encoder) at each time step -> the path from an input word to its translation is now much shorter, so the short-term memory limitations of RNNs have much less impact, Alignment model / attention layer: small neural network trained jointly with the rest of the Encoder-Decoder model, Generate image captions using visual atention: a CNN processes the image and outputs some feature maps, then a decoded RNN with an attention mechanism generates the caption, one word at a time, Explainability: Attention mechanisms make it easier to understand what led the model to produce its output -> especially useful when the model makes a mistake (check what the model focused on). ```python Improved state of the art NMT without using recurrent or convolutional layers, just attention mechanisms. This paper. It often achieves higher performance than hard voting because it gives more weight to highly confident votes. pdf hands on machine learning with scikit learn. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition $14.39 Sale Price So predictions are very fast, even when dealing with large training sets. Ridge is a good default, but if you suspect that only a few features are useful, you should prefer Lasso or Elastic Net because they tend to reduce the useless features’ weights down to zero. Even if each classifier is a weak learner (meaning it does only slightly better than random guessing), the ensemble can still be a strong learner (achieving high accuracy), provided there are a sufficient number of weak learners and they are sufficiently diverse. if needed), Optimizer - Momentum optimization (or RMSProp or Nadam), Kernel initializer - LeCun initialization, Normalization - None (self-normalization). ... Download PDF. logarithm). e.g., videos safe for kids: prefer reject many good videos (low recall), but keeps only safe ones (high precision), Scikit-Learn gives you access to the decision scores that it uses to make predicitions, .decision_function() method, which returns a score for each instance and then use any threshold you want to make predictions based on those scores.
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