Deep and Reinforcement Learning 

Deep learning has been a famous word in the machine learning world in recent times. The main goal of the deep learning algorithm is to use machine learning to achieve Artificial General Intelligence (AGI), It shows human-level intelligence in machines to solve any problems for a given area. Deep learning has shown promising computer vision, audio processing, and text mining outcomes. The advancements in AI field have many great examples such as self-driving cars. You’ll learn about deep learning’s concept, evolution (Perceptron to Convolution Neural Network), key applications, implementation, and other things There have been many powerful and popular open-source libraries built in the last few years that focus on deep learning.

Their official websites provide information with quality documentation and examples. I will recommend visiting the respective site to learn more.  It is a Python library predominantly developed by academics at University de Montreal. Theano allows you to efficiently define, optimize, and evaluate mathematical expressions involving complex multidimensional arrays. It is designed to work with a Graphical processing unit and perform efficient differentiation. It is fast and stable.

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TensorFlow: It is a library for numerical computation using data flow graphs for machine learning and developed by Google researchers. It is used by Google products for research and production purposes. It was open-sourced in 2015 and has gained wide popularity in the machine learning world.

Pylearn2: A new machine learning library based on Theano, which means users can write algorithms by using mathematical expressions, and Theano will optimize, stabilize, and compile those expressions.

Kera’s: It is known as a high-level neural network library, written in Python and capable of running on top of either TensorFlow or Theano. It’s an interface not just an end-end machine learning framework. It’s written in Python, simple to start, highly module, and deep enough to expand to support more complex models.

MX Net: It was developed in collaboration with researchers from CMU, NYU, NUS, and MIT. It is a lightweight, Portable, Flexible Distributed library supported by languages such as Python, R, Julia, Scala, Go, JavaScript, etc.

Caffe: It is a deep learning framework by Berkeley Vision and Learning Centre (BVLC) written in C++ and has python/MATLAB buildings.

Lasagne: It is a library to build and train neural networks in Theano. Throughout this chapter ‘Scikit-learn’ and ‘Keras’ library with back end as TensorFlow or Theano appropriately has been used, because these are the best choices for a beginner to get hold of the concepts. Also, these are most widely used by machine learning practitioners.

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Before starting into details of deep learning, it is very important to understand how human vision works. Our human brain is a vast complex connected neural network where different regions of the brain are responsible for different tasks, and these regions are machines of the brain that receive signals after processing them you can take necessary action. Our brain is full of a cluster of small connected units named neurons, which send electrical signals to another. Long-term knowledge is the best way to represent by using the strength of the connections between neurons. We see objects, light travels through the retina, the visual information will get converted to other signals, and further on the electric signal passes through the hierarchy of connected neurons of different regions within the brain in a few milliseconds to decode signals/information. Perceptron – Single Artificial Neuron Inspired by the biological neurons, McCulloch and Pitts 1943 introduced the concept of perceptron as an artificial neuron that is the basic building block of the artificial neural network. They are not only named after their biological counterparts but also modelled after the behaviour of the neurons in our brain.

Biological neurons contain dendrites to receive signals, a cell body to process them, and an axon terminal to transfer signals out to other neurons. An artificial neuron has multiple input channels to accept training samples represented as a vector, and a processing stage where the weights(w) are adjusted such that the output error (actual vs. predicted) is minimized.  The result is input to an activation function to produce output, an example is a classification label. The activation function for a classification problem is a threshold cut-off above which class is 1 else 0. 

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Multilayer Perceptron’s –

To address the drawback of single perceptron’s, multilayer perceptron’s were proposed; also, commonly known as a feedforward neural network, it is a composition of multiple perceptron’s connected in different ways and operating on distinctive activation functions is to improve learning mechanisms. The training sample data propagates forward through the network and the output error is backpropagated and the loss is minimized by using the gradient descent method other optimizers are there also, which will calculate a loss function for all the weights in the network.

Author:-

Suraj Kale

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