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7 min readCallbacks are a feature in TensorFlow that allow you to perform certain actions during the training process. One common use of callbacks is for implementing early stopping, which helps prevent overfitting and saves computational resources by stopping training when the model's performance stops improving.To use callbacks for early stopping in TensorFlow, you need to define a callback object and pass it to the .fit() function during model training.
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8 min readOverfitting is a common problem that occurs when a machine learning model learns the training data too well, to the point that it fails to generalize well to new, unseen data. TensorFlow offers several techniques to handle overfitting in models.One approach is to use regularization techniques such as L1 and L2 regularization, which add a penalty term to the loss function during training.
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10 min readTrading with Typical Price involves using a simple technical indicator to analyze price movements and make trading decisions. The Typical Price is calculated by averaging the high, low, and closing price of a trading instrument over a specified period.To trade with the Typical Price, here is a general guideline:Calculate the Typical Price: Add the high, low, and closing price of the trading instrument and divide the sum by 3. This will give you the Typical Price for a specific period.
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7 min readBatch normalization is a technique used to improve the speed, stability, and performance of neural networks. It works by normalizing the output of the previous layer within each batch of training examples. This helps in mitigating the issue of internal covariate shift, where the distribution of the input to each layer changes during training.To implement batch normalization in TensorFlow, follow these steps:Import the necessary TensorFlow libraries: import tensorflow as tf from tensorflow.keras.
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10 min readThe Detrended Price Oscillator (DPO) is a technical indicator that helps traders in analyzing price movements without the influence of any trend. It helps identify cycles and short-term price patterns independent of longer-term trends.To interpret the DPO, traders should consider the following key points:Range bound indicator: DPO oscillates around the zero line, which represents the average price over a specified period, usually a number of days.
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6 min readTensorBoard is a powerful visualization tool provided by TensorFlow. It allows you to visualize, analyze, and debug your machine learning models. With TensorBoard, you can track and visualize various aspects of your model's training and evaluation, such as the loss, accuracy, weights, and model architecture.To use TensorBoard for model visualization, you need to follow these steps:Import the necessary libraries: You need to import TensorFlow and any other libraries required for your model.
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11 min readTypical price refers to the average or common price of a product or service within a specific market or industry. It is the usual amount that customers are expected to pay for a particular item, considering factors like demand, supply, production costs, competition, and market conditions.Determining the typical price involves analyzing various variables that impact pricing.
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5 min readDeploying a TensorFlow model to production involves a series of steps to ensure that the model is ready to be used in a real-world application. Here is an overview of the deployment process:Train and save the model: Begin by training and fine-tuning your TensorFlow model using an appropriate dataset. Once the model is trained, save it in a portable format like SavedModel or HDF5.
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11 min readThe Volume Price Trend (VPT) is a technical analysis indicator used in the stock market to assess the relationship between price and volume. It helps traders and investors determine the strength of a price trend and identify possible reversals.Developed by Buff Dormeier in the 1990s, the VPT combines volume and price data to provide insights into the buying and selling pressure behind price movements.
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8 min readTransfer learning is a technique in machine learning where a pre-trained model is used as a starting point for training a new model. This allows the new model to leverage the knowledge gained by the pre-trained model and speeds up the training process.TensorFlow provides a convenient interface for performing transfer learning. The first step is to load a pre-trained model using the tf.keras.applications module.
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9 min readVisualizing training progress in TensorFlow can be helpful in gaining insights into the model's performance, identifying potential issues, and making informed decisions to improve the training process. Here are some ways to visualize training progress in TensorFlow:TensorBoard: TensorBoard is a powerful visualization tool provided by TensorFlow. It allows you to monitor and analyze various aspects of your model's training progress.