Institute of AI

  Keras If you like the Python-way of doing things, Keras is for you. It is a high-level library for neural networks, using TensorFlow or Theano as its backend.      The majority of practical problems are more like: picking an architecture suitable for a problem, for image recognition problems – using weights trained on ImageNet, configuring a network to optimize the results (a long, iterative process). In all of these, Keras is a gem. Also, it offers an abstract structure that can be easily converted to other frameworks, if needed (for compatibility, performance, or anything).

  MxNet It allows for trading computation time for memory via ‘forgetful backprop’ which can be very useful for recurrent nets on very long sequences.     Built with scalability in mind (fairly easy-to-use support for multi-GPU and multi-machine training). Lots of cool features, like easily writing custom layers in high-level languages Unlike almost all other major frameworks, it is not directly governed by a major corporation which is a healthy situation for an open-source, community-developed framework. TVM support, which will further improve deployment support, and allow running on a whole host of new device types

  Caffe       ‘Caffe’ is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is created by the Berkeley Vision and Learning Center (BVLC) and by network donors. Google’s DeepDream depends on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

  Theano Theano is wonderfully folded over Keras, an abnormal state neural systems library, that runs nearly in parallel with the Theano library. Keras’ fundamental favorable position is that it is a moderate Python library for profound discovery that can keep running over Theano or TensorFlow. It was created to make actualizing profound learning models as quick and simple as feasible for innovative work. It keeps running on Python 2.7 or 3.5 and can consistently execute on GPUs and CPUs.   What sets Theano separated is that it exploits the PC’s GPU. This enables it to make information escalated counts up to multiple times quicker than when kept running on the CPU alone. Theano’s speed makes it particularly profitable for profound learning and other computationally complex undertakings.

  Tensorflow On the off chance that you are in the realm of Artificial Intelligence, you have most likely found out about, attempted or executed some type of profound learning calculation. Is it accurate to say that they are essential? Not constantly. Is it accurate to say that they are cool when done right? Truly!  The fascinating thing about Tensorflow is that when you compose a program in Python, you can arrange and keep running on either your CPU or GPU. So you don’t need to compose at the C++ or CUDA level to keep running on GPUs.  It utilizes an arrangement of multi-layered hubs that enables you to rapidly set up, train, and send counterfeit neural systems with huge datasets. This is the thing that enables Google to recognize questions in photographs or comprehend verbally expressed words in its voice-acknowledgment application.

  Scikit Learn Scikit-learn is one of the most well-known ML libraries. It underpins many administered and unsupervised learning calculations. Precedents incorporate direct and calculated relapses, choice trees, bunching, k-implies, etc.     It expands on two essential libraries of Python, NumPy and SciPy. It includes a lot of calculations for regular AI and data mining assignments, including bunching, relapse and order. Indeed, even undertakings like changing information, feature determination and ensemble techniques can be executed in a couple of lines. For a fledgeling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you begin actualizing progressively complex calculations.

 AI Usage in banking AI Banking Index The Evident AI Index assesses the various approaches businesses are taking towards AI readiness, starting with banks. As of January 2023, the Index covers the largest 23 banks in North America and Europe. Each bank is assessed on 143 individual indicators drawn from millions of publicly available data points.