How important is TensorFlow for machine learning and AI and what are other major frameworks?
TensorFlow has played a significant role in the evolution and adoption of machine learning (ML) and artificial intelligence (AI) methodologies within both academic and industrial domains. Developed and open-sourced by Google Brain in 2015, TensorFlow was designed to facilitate the construction, training, and deployment of neural networks and other machine learning models at scale. Its
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Fundamentals of machine learning
How Keras models replace TensorFlow estimators?
The transition from TensorFlow Estimators to Keras models represents a significant evolution in the workflow and paradigm of machine learning model creation, training, and deployment, particularly within the TensorFlow and Google Cloud ecosystems. This change is not merely a shift in API preference but reflects broader trends in accessibility, flexibility, and the integration of modern
What is underfitting?
Underfitting is a concept in machine learning and statistical modeling that describes a scenario where a model is too simple to capture the underlying structure or patterns present in the data. In the context of computer vision tasks using TensorFlow, underfitting emerges when a model, such as a neural network, fails to learn or represent
- Published in Artificial Intelligence, EITC/AI/TFF TensorFlow Fundamentals, Introduction to TensorFlow, Basic computer vision with ML
Which parameters indicate that it's time to switch from a linear model to deep learning?
Determining when to transition from a linear model to a deep learning model is an important decision in the field of machine learning and artificial intelligence. This decision hinges on a multitude of factors that include the complexity of the task, the availability of data, computational resources, and the performance of the existing model. Linear
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
What is a one-hot vector?
In the domain of deep learning and artificial intelligence, particularly when implementing models using Python and PyTorch, the concept of a one-hot vector is a fundamental aspect of encoding categorical data. One-hot encoding is a technique used to convert categorical data variables so they can be provided to machine learning algorithms to improve predictions. This
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Advancing with deep learning, Computation on the GPU
What is a deep neural network?
A deep neural network (DNN) is a type of artificial neural network (ANN) characterized by multiple layers of nodes, or neurons, that enable the modeling of complex patterns in data. It is a foundational concept in the field of artificial intelligence and machine learning, particularly in the development of sophisticated models that can perform tasks
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, TensorBoard for model visualization
What tools exists for XAI (Explainable Artificial Intelligence)?
Explainable Artificial Intelligence (XAI) is a important aspect of modern AI systems, particularly in the context of deep neural networks and machine learning estimators. As these models become increasingly complex and are deployed in critical applications, understanding their decision-making processes becomes imperative. XAI tools and methodologies aim to provide insights into how models make predictions,
- Published in Artificial Intelligence, EITC/AI/GCML Google Cloud Machine Learning, First steps in Machine Learning, Deep neural networks and estimators
Does one need to initialize a neural network in defining it in PyTorch?
When defining a neural network in PyTorch, the initialization of network parameters is a critical step that can significantly affect the performance and convergence of the model. While PyTorch provides default initialization methods, understanding when and how to customize this process is important for advanced deep learning practitioners aiming to optimize their models for specific
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Responsible innovation, Responsible innovation and artificial intelligence
Does a torch.Tensor class specifying multidimensional rectangular arrays have elements of different data types?
The `torch.Tensor` class from the PyTorch library is a fundamental data structure used extensively in the field of deep learning, and its design is integral to the efficient handling of numerical computations. A tensor, in the context of PyTorch, is a multi-dimensional array, similar in concept to arrays in NumPy. However, it is important to
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Responsible innovation, Responsible innovation and artificial intelligence
Is the rectified linear unit activation function called with rely() function in PyTorch?
The rectified linear unit, commonly known as ReLU, is a widely used activation function in the field of deep learning and neural networks. It is favored for its simplicity and effectiveness in addressing the vanishing gradient problem, which can occur in deep networks with other activation functions like the sigmoid or hyperbolic tangent. In PyTorch,
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Responsible innovation, Responsible innovation and artificial intelligence