Deep Learning is a subfield of Machine Learning based on Artificial Neural Networks. Unsupervised Pretrained Networks (UPNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks, and Recursive Neural Networks are the four major network architectures in Deep Learning. One of the main differences between Deep Learning vs Machine Learning is from the way data is delivered to the system. It has more computational power making it one of the powerful and important Machine Learning subsets.
There are many Deep Learning Frameworks; however, we are going to review and list the most preferred frameworks.
Developed and maintained by Google, Tensorflow is the best and most popular Deep Learning Frameworks. It can be used for building and deploying Deep Learning models. It can be used with Keras to build Neural Networks line-by-line.
Primarily developed by Facebook's AI Research lab, PyTorch is another library used to develop and train Neural Networks. It is a Python package and can be used with Python and C++. It is one of the preferred Deep Learning libraries for researchers.
Keras is a Python-based open-source neural-network library. It is a high-level API built on Theano and Tensorflow and is considered to be more user-friendly.
Apache MXNet is an open-source deep learning software framework developed by the Apache Software Foundation. MXNet stands for Mix and Maximize. Its fast problem-solving ability makes it an efficient Deep Learning library to train and deploy Neural Networks.
Written in C++ and Python, Caffe is a CAFFE is a Deep Learning framework developed by Berkeley AI Research (BAIR). Caffe is primarily designed for Speed, making it a perfect choice for research experiments and industry deployments.
If you are a Data Science enthusiast who intends to learn and master Computer Vision concepts and their applications using Deep Learning, this book is for you.
Fundamentals of Deep Learning and Computer Vision will serve as a basic guide for all the beginners to master Deep Learning and Computer Vision with lucid and intuitive explanations using basic mathematical concepts. It also explores these concepts with the popular Deep Learning framework TensorFlow.
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