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TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]
Comments can explain the author's intent – why the code is as it is. Some contend that describing what the code does is superfluous. The need to explain the what is a sign that it is too complex and should be re-worked. "Don't document bad code – rewrite it." [9] "Good comments don't repeat the code or explain it.
These libraries also have support for code generation for embedded hardware. C++ is a compiled language that can interact with low-level hardware. In the context of AI, it is particularly used for embedded systems and robotics. Libraries such as TensorFlow C++, Caffe or Shogun can be used. [1]
TensorFlow is an open-source numerical computing framework that allows you preprocess data, model data (find patterns in it, typically with deep learning) and deploy your solutions to the world.
It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. [5] [6] The primary functions of JAX are: [2] grad: automatic differentiation; jit: compilation; vmap: auto-vectorization; pmap: Single program, multiple data (SPMD) programming
"Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with one codebase."
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words.