Deep learning, an artificial intelligence (AI) subset, mirrors the functions and structure of the human brain. It adapts to unstructured input, using intricate algorithms to condition a neural network. Deep learning employs neural networks extensively, training them to discern text, numbers, pictures, audio, and other data formats. This data is usually complex, unstructured, and diverse, including audio, text files, and photos.
Deep learning libraries
Notable libraries for machine learning and deep learning application coding exist. Among them include Keras, Theano, TensorFlow, DL4J, and Torch, which are widely used. These libraries present array of options for the user, with Google's TensorFlow gaining increased recognition owing to its open-source nature. Initially, Keras was a popular choice but has since merged with TensorFlow.
Python remains the most suitable and widely used language, despite TensorFlow supporting various languages.
TensorFlow, a deep learning application open-source library, is a Google product. It also supports traditional machine learning. Originally created for sizable numerical computations rather than deep learning, TensorFlow's highly beneficial nature for deep learning projects led to Google open-sourcing it.
Data in TensorFlow comes in tensors, or high-dimension multi-dimensional arrays. For vast data volumes, employing these arrays is advantageous.
TensorFlow operates on data flow representations of nodes and edges in graphs. The execution mechanism, in graph form, makes TensorFlow code easier to implement across a computer cluster using GPUs.
The TensorFlow Advantage
TensorFlow has APIs in C++ and Python, Java, and R integration, which simplifies the coding process for machine learning and deep learning.
Unlike other libraries, TensorFlow supports both CPUs and GPUs. Deep learning programs are intricately complex and necessitate high computing power to train due to extensive data size and the multiple iterative procedures involved, which comprise mathematical computing and matrix multiplications. Regular Central Processing Units (CPUs) would normally take considerably more time to execute these tasks.
Graphical Processing Units (GPUs), primarily designed for graphical needs in games, are also employed in deep learning applications. TensorFlow's compatibility with both GPUs and CPUs is a major strength and generally compiles faster than other libraries, including Keras and Torch.