Deep learning
Cloud Deep Learning: 3 Focus Areas & Key Things to Know in '24
Cloud deep learning is the integration of cloud computing and deep learning models that can process inputs through different layers. By developing deep learning, businesses can perform more complex tasks compared to classical algorithms. There are various facets to achieve this goal.
4 Ways to Overcome Deep Learning Challenges in 2024
Deep learning presents great opportunities for businesses with powerful applications such as image classification, anomaly detection, and voice recognition. However, only 16% of companies have taken deep learning projects beyond the pilot stage. Applying deep learning models to business use cases poses some challenges, and it is essential to be aware of them to avoid spending resources on failed projects.
In-Depth Guide to Deep Learning Consulting in 2024
Applications of deep learning enable businesses from a wide range of industries to develop innovative products, increase revenues, and reduce costs. However, developing and implementing a successful deep learning application has its own set of challenges. Benefiting from deep learning consulting can help businesses overcome these challenges and reduce the chance of spending resources on failed projects.
In-Depth Guide to Recurrent Neural Networks (RNNs) in 2024
Neural networks are powering a wide range of deep learning applications in different industries with use cases such as natural language processing (NLP), computer vision and drug discovery. There are different types of neural networks for different applications such as: In this article, we will explore RNNs and their use cases.
Generative Adversarial Networks (GAN) & Synthetic Data [2024]
Generative Adversarial Network (GAN) is a type of generative model based on deep neural networks. You may have heard of it as the algorithm behind the artificially created portrait painting, Edmond de Bellamy, which was sold for $432,500 in 2018.
Synthetic Data to Improve Deep Learning Models in 2024
Despite its success in a wide range of tasks, deep learning has an important limitation: its data-hungry nature. Collecting and labeling huge data with desired properties is costly, time-consuming, or unfeasible in some applications. Synthetic data, also called artificially generated data, can help improve the performance of deep learning algorithms by meeting their data demands.
What is Deep Learning? Use Cases, Examples, Benefits in 2024
Deep learning is a state-of-the-art field in machine learning domain. Deep learning models can learn from examples and they need to be trained with sufficient data. The predictions of deep learning algorithms can boost the performance of businesses.
Data Augmentation to Improve Deep Learning Models in 2024
Performance and accuracy level of deep learning models depend on the volume and diversity of training data which is used to feed and train neural network architectures. Therefore, challenges in collecting and labelling of training data can limit development of deep learning models.
Future of Deep Learning according to top AI Experts of 2024
Deep learning is currently the most effective AI technology for numerous applications. However, there is still differing opinions on how capable deep learning can become.
Top 7 Deep Learning Applications in Manufacturing in 2024
Deep learning solutions are transforming manufacturing companies into high-efficiency organizations by these benefits: improve productivity decrease production defects increase capacity utilization reduce maintenance costs What is the level of interest in using deep learning in manufacturing? Deep learning is a good fit for manufacturing because manufacturing produces significant levels of data (e.g.