Python vs. TFLearn vs. Tcl vs. V Programming Language Comparison Chart

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks.

Simple, fast, safe, and compiled. For developing maintainable software. Simple language for building maintainable programs. You can learn the entire language by going through the documentation over a weekend, and in most cases, there's only one way to do something. This results in simple, readable, and maintainable code. This results in simple, readable, and maintainable code. Despite being simple, V gives a lot of power to the developer and can be used in pretty much every field, including systems programming, webdev, gamedev, GUI, mobile, science, embedded, tooling, etc. V is very similar to Go. If you know Go, you already know 80% of V. Bounds checking, No undefined values, no variable shadowing, immutable variables by default, immutable structs by default, option/result and mandatory error checks, sum types, generics, and immutable function args by default, mutable args have to be marked on call.