Is the usually recommended data split between training and evaluation close to 80% to 20% correspondingly?
The usual split between training and evaluation in machine learning models is not fixed and can vary depending on various factors. However, it is generally recommended to allocate a significant portion of the data for training, typically around 70-80%, and reserve the remaining portion for evaluation, which would be around 20-30%. This split ensures that
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Further steps in Machine Learning, Big data for training models in the cloud
Can Tensorflow be used for training and inference of deep neural networks (DNNs)?
TensorFlow is a widely-used open-source framework for machine learning developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources that enable developers and researchers to build and deploy machine learning models efficiently. In the context of deep neural networks (DNNs), TensorFlow is not only capable of training these models but also facilitating
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, Advancing in Machine Learning, TensorFlow Hub for more productive machine learning
What is the purpose of iterating over the dataset multiple times during training?
When training a neural network model in the field of deep learning, it is common practice to iterate over the dataset multiple times. This process, known as epoch-based training, serves a crucial purpose in optimizing the model's performance and achieving better generalization. The main reason for iterating over the dataset multiple times during training is
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Neural network, Training model, Examination review
What is the structure of the neural machine translation model?
The neural machine translation (NMT) model is a deep learning-based approach that has revolutionized the field of machine translation. It has gained significant popularity due to its ability to generate high-quality translations by directly modeling the mapping between source and target languages. In this answer, we will explore the structure of the NMT model, highlighting
How is the output of the neural network model represented in the AI Pong game?
In the AI Pong game implemented using TensorFlow.js, the output of the neural network model is represented in a way that enables the game to make decisions and respond to the player's actions. To understand how this is achieved, let's delve into the details of the game mechanics and the role of the neural network
How do we train our network using the `fit` function? What parameters can be adjusted during training?
The `fit` function in TensorFlow is used to train a neural network model. Training a network involves adjusting the weights and biases of the model's parameters based on the input data and the desired output. This process is known as optimization and is crucial for the network to learn and make accurate predictions. To train
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Using convolutional neural network to identify dogs vs cats, Training the network, Examination review
What is the purpose of checking if a saved model already exists before training?
When training a deep learning model, it is important to check if a saved model already exists before starting the training process. This step serves several purposes and can greatly benefit the training workflow. In the context of using a convolutional neural network (CNN) to identify dogs vs cats, the purpose of checking if a
How is the action chosen during each game iteration when using the neural network to predict the action?
During each game iteration when using a neural network to predict the action, the action is chosen based on the output of the neural network. The neural network takes in the current state of the game as input and produces a probability distribution over the possible actions. The chosen action is then selected based on
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Training a neural network to play a game with TensorFlow and Open AI, Testing network, Examination review
How do we create the input layer in the neural network model definition function?
To create the input layer in the neural network model definition function, we need to understand the fundamental concepts of neural networks and the role of the input layer in the overall architecture. In the context of training a neural network to play a game using TensorFlow and OpenAI, the input layer serves as the
What is the goal of machine learning and how does it differ from traditional programming?
The goal of machine learning is to develop algorithms and models that enable computers to automatically learn and improve from experience, without being explicitly programmed. This differs from traditional programming, where explicit instructions are provided to perform specific tasks. Machine learning involves the creation and training of models that can learn patterns and make predictions
- Published in Artificial Intelligence, EITC/AI/DLTF Deep Learning with TensorFlow, Training a neural network to play a game with TensorFlow and Open AI, Introduction, Examination review