Tyshawn  Braun

Tyshawn Braun

1602954000

Perceptron to Multi-layered Feedforward Neural Network

This article will help you understand the transition of AI from classical machine learning to deep learning, starting from the basics of machine learning with its major - supervised and unsupervised learning to different regularization and optimization techniques.

The goal of this article is to introduce the major concepts of machine learning-what is machine learning? How does a machine learn by itself to do a particular task? How does it choose the essential features of the data that strongly contribute to the prediction of future events? How can we understand whether a machine has succeeded or failed in that?

In this article, we will discuss the foundations of artificial neural networks starting from perceptron to multi-layered feedforward neural networks. This article discusses the various stages of how to apply different transformation techniques for data preparation, how to train a neural network, and then validate and deploy a neural network for solving real-world problems.

In this article, we’re going to cover the following main topics:

· Understanding Machine Learning and Artificial Neural Network

· Feedforward Neural Network & Backpropagation Algorithm

· Evaluating and Tuning the Artificial Neural Network

· Classical Machine Learning vs Deep Learning

Understanding Machine Learning and Artificial Neural Network

This section starts with a brief overview of what is machine learning, its major types- supervised and unsupervised learning. Then we will understand the very evolution of artificial neural networks starting with how a biological neuron works. We’ll also discuss the design of artificial neurons with an understanding of deep neural networks with activation functions.

What is Machine Learning?

The term, Machine learning, has become a buzzword nowadays which refers to the ability of a machine to learn from the data without the help of the set of rules that are defined explicitly as like in the traditional rule-based algorithms. So definitely, if it learns from the data without any need for an explicit declaration of rules then it has to do with the experience from learning.

Our way of learning always follows a curve of failures although it’s perfectly descendent, lastly it will converge to the extent of our hard work

In the last decade of technology, machine learning techniques have become the common tools to automate the tasks that would have required huge efforts with the traditional rule-based algorithms.

In the** traditional rule-based algorithms**, the set of rules used to be defined to work on with a specific variety of data and could not be generalized to a large extent of data because of its specificity of working on only particular data. For example, if YouTube, a video sharing site decides to perform a copyright check on videos that are being uploaded on its server with a human operator, it will need a lot many people to execute this task of copyright check. But if YouTube chooses to do this with the help of some video processing algorithm then the task of copyright check would be easier but not robust as video processing algorithm possibly would work only on a set of videos that don’t have any kind of transformations like flip, rotate, crop, blur, etc. And it’s quite difficult to write a separate algorithm for individual transformation so the solution to this problem can be machine learning. In this case, a learning model is built by getting trained on data and identifying implicit features that uniquely signifies the data with which new data can be validated automatically.

Today, we are living in the era of machine-learning-based technologies; email services learn how to classify the emails into spam and ham; search engines learn what to recommend to the user based on their search history; banking systems are now able to sanction loans based on the creditworthiness of a customer. Prediction of heart disease based on clinical data, identifying voice commands, and forecasting annual rainfall are other significant tasks that machine learning facilitates.

One common problem with all of these applications is that a programmer cannot explicitly define the set of instructions for the task that needs to be performed due to the underlying complexity of the data; this was machine learning helps. It has made itself useful across industries like retail, banking, healthcare to the automobile industry for its ability to predict future events with significant accuracy.

_In machine learning algorithms, the __input is the experience in form of data _and output is knowledge or wisdom gained with inductive inference which in turn helps to predict future events, so rather, machine learning is an art of experiential learning.

Let us start with a real-life experience of preparing a food dish with some cooking recipe, how do we prepare the food, let’s go through the process of making the delicious food, at first we collect all the ingredients that are needed for food preparation, as a naïve person in the cooking, we follow a cooking recipe which involves set of steps that need to be performed. Let us take an example of a famous dish of western India, poha, which needs many ingredients like beaten rice flakes, mustard, curry leaves, groundnuts, oil, salt, and others. Assume now, with all ingredients, we start making the poha as per the directions in the cooking recipe.

#artificial-intelligence #machine-learning #artificial-neural-network #neural-networks #deep-learning

What is GEEK

Buddha Community

Perceptron to Multi-layered Feedforward Neural Network
Tyshawn  Braun

Tyshawn Braun

1602954000

Perceptron to Multi-layered Feedforward Neural Network

This article will help you understand the transition of AI from classical machine learning to deep learning, starting from the basics of machine learning with its major - supervised and unsupervised learning to different regularization and optimization techniques.

The goal of this article is to introduce the major concepts of machine learning-what is machine learning? How does a machine learn by itself to do a particular task? How does it choose the essential features of the data that strongly contribute to the prediction of future events? How can we understand whether a machine has succeeded or failed in that?

In this article, we will discuss the foundations of artificial neural networks starting from perceptron to multi-layered feedforward neural networks. This article discusses the various stages of how to apply different transformation techniques for data preparation, how to train a neural network, and then validate and deploy a neural network for solving real-world problems.

In this article, we’re going to cover the following main topics:

· Understanding Machine Learning and Artificial Neural Network

· Feedforward Neural Network & Backpropagation Algorithm

· Evaluating and Tuning the Artificial Neural Network

· Classical Machine Learning vs Deep Learning

Understanding Machine Learning and Artificial Neural Network

This section starts with a brief overview of what is machine learning, its major types- supervised and unsupervised learning. Then we will understand the very evolution of artificial neural networks starting with how a biological neuron works. We’ll also discuss the design of artificial neurons with an understanding of deep neural networks with activation functions.

What is Machine Learning?

The term, Machine learning, has become a buzzword nowadays which refers to the ability of a machine to learn from the data without the help of the set of rules that are defined explicitly as like in the traditional rule-based algorithms. So definitely, if it learns from the data without any need for an explicit declaration of rules then it has to do with the experience from learning.

Our way of learning always follows a curve of failures although it’s perfectly descendent, lastly it will converge to the extent of our hard work

In the last decade of technology, machine learning techniques have become the common tools to automate the tasks that would have required huge efforts with the traditional rule-based algorithms.

In the** traditional rule-based algorithms**, the set of rules used to be defined to work on with a specific variety of data and could not be generalized to a large extent of data because of its specificity of working on only particular data. For example, if YouTube, a video sharing site decides to perform a copyright check on videos that are being uploaded on its server with a human operator, it will need a lot many people to execute this task of copyright check. But if YouTube chooses to do this with the help of some video processing algorithm then the task of copyright check would be easier but not robust as video processing algorithm possibly would work only on a set of videos that don’t have any kind of transformations like flip, rotate, crop, blur, etc. And it’s quite difficult to write a separate algorithm for individual transformation so the solution to this problem can be machine learning. In this case, a learning model is built by getting trained on data and identifying implicit features that uniquely signifies the data with which new data can be validated automatically.

Today, we are living in the era of machine-learning-based technologies; email services learn how to classify the emails into spam and ham; search engines learn what to recommend to the user based on their search history; banking systems are now able to sanction loans based on the creditworthiness of a customer. Prediction of heart disease based on clinical data, identifying voice commands, and forecasting annual rainfall are other significant tasks that machine learning facilitates.

One common problem with all of these applications is that a programmer cannot explicitly define the set of instructions for the task that needs to be performed due to the underlying complexity of the data; this was machine learning helps. It has made itself useful across industries like retail, banking, healthcare to the automobile industry for its ability to predict future events with significant accuracy.

_In machine learning algorithms, the __input is the experience in form of data _and output is knowledge or wisdom gained with inductive inference which in turn helps to predict future events, so rather, machine learning is an art of experiential learning.

Let us start with a real-life experience of preparing a food dish with some cooking recipe, how do we prepare the food, let’s go through the process of making the delicious food, at first we collect all the ingredients that are needed for food preparation, as a naïve person in the cooking, we follow a cooking recipe which involves set of steps that need to be performed. Let us take an example of a famous dish of western India, poha, which needs many ingredients like beaten rice flakes, mustard, curry leaves, groundnuts, oil, salt, and others. Assume now, with all ingredients, we start making the poha as per the directions in the cooking recipe.

#artificial-intelligence #machine-learning #artificial-neural-network #neural-networks #deep-learning

Adaline  Kulas

Adaline Kulas

1594162500

Multi-cloud Spending: 8 Tips To Lower Cost

A multi-cloud approach is nothing but leveraging two or more cloud platforms for meeting the various business requirements of an enterprise. The multi-cloud IT environment incorporates different clouds from multiple vendors and negates the dependence on a single public cloud service provider. Thus enterprises can choose specific services from multiple public clouds and reap the benefits of each.

Given its affordability and agility, most enterprises opt for a multi-cloud approach in cloud computing now. A 2018 survey on the public cloud services market points out that 81% of the respondents use services from two or more providers. Subsequently, the cloud computing services market has reported incredible growth in recent times. The worldwide public cloud services market is all set to reach $500 billion in the next four years, according to IDC.

By choosing multi-cloud solutions strategically, enterprises can optimize the benefits of cloud computing and aim for some key competitive advantages. They can avoid the lengthy and cumbersome processes involved in buying, installing and testing high-priced systems. The IaaS and PaaS solutions have become a windfall for the enterprise’s budget as it does not incur huge up-front capital expenditure.

However, cost optimization is still a challenge while facilitating a multi-cloud environment and a large number of enterprises end up overpaying with or without realizing it. The below-mentioned tips would help you ensure the money is spent wisely on cloud computing services.

  • Deactivate underused or unattached resources

Most organizations tend to get wrong with simple things which turn out to be the root cause for needless spending and resource wastage. The first step to cost optimization in your cloud strategy is to identify underutilized resources that you have been paying for.

Enterprises often continue to pay for resources that have been purchased earlier but are no longer useful. Identifying such unused and unattached resources and deactivating it on a regular basis brings you one step closer to cost optimization. If needed, you can deploy automated cloud management tools that are largely helpful in providing the analytics needed to optimize the cloud spending and cut costs on an ongoing basis.

  • Figure out idle instances

Another key cost optimization strategy is to identify the idle computing instances and consolidate them into fewer instances. An idle computing instance may require a CPU utilization level of 1-5%, but you may be billed by the service provider for 100% for the same instance.

Every enterprise will have such non-production instances that constitute unnecessary storage space and lead to overpaying. Re-evaluating your resource allocations regularly and removing unnecessary storage may help you save money significantly. Resource allocation is not only a matter of CPU and memory but also it is linked to the storage, network, and various other factors.

  • Deploy monitoring mechanisms

The key to efficient cost reduction in cloud computing technology lies in proactive monitoring. A comprehensive view of the cloud usage helps enterprises to monitor and minimize unnecessary spending. You can make use of various mechanisms for monitoring computing demand.

For instance, you can use a heatmap to understand the highs and lows in computing visually. This heat map indicates the start and stop times which in turn lead to reduced costs. You can also deploy automated tools that help organizations to schedule instances to start and stop. By following a heatmap, you can understand whether it is safe to shut down servers on holidays or weekends.

#cloud computing services #all #hybrid cloud #cloud #multi-cloud strategy #cloud spend #multi-cloud spending #multi cloud adoption #why multi cloud #multi cloud trends #multi cloud companies #multi cloud research #multi cloud market

Mckenzie  Osiki

Mckenzie Osiki

1623135499

No Code introduction to Neural Networks

The simple architecture explained

Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks

Marlon  Boyle

Marlon Boyle

1594312560

Autonomous Driving Network (ADN) On Its Way

Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.

Image for post

Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.

The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.

Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.

In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.

The Vision

At the current stage, there are different terminologies to describe ADN vision by various organizations.
Image for post

Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.

On the network layer, it contains the below critical aspects:

  • Intent-Driven: Understand the operator’s business intent and automatically translate it into necessary network operations. The operation can be a one-time operation like disconnect a connection service or continuous operations like maintaining a specified SLA (Service Level Agreement) at the all-time.
  • **Self-Discover: **Automatically discover hardware/software changes in the network and populate the changes to the necessary subsystems to maintain always-sync state.
  • **Self-Config/Self-Organize: **Whenever network changes happen, automatically configure corresponding hardware/software parameters such that the network is at the pre-defined target states.
  • **Self-Monitor: **Constantly monitor networks/services operation states and health conditions automatically.
  • Auto-Detect: Detect network faults, abnormalities, and intrusions automatically.
  • **Self-Diagnose: **Automatically conduct an inference process to figure out the root causes of issues.
  • **Self-Healing: **Automatically take necessary actions to address issues and bring the networks/services back to the desired state.
  • **Self-Report: **Automatically communicate with its environment and exchange necessary information.
  • Automated common operational scenarios: Automatically perform operations like network planning, customer and service onboarding, network change management.

On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).

No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.

David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):

“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”

Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)

“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”

Is This Vision Achievable?

If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:

  • software-defined networking (SDN) control
  • industry-standard models and open APIs
  • Real-time analytics/telemetry
  • big data processing
  • cross-domain orchestration
  • programmable infrastructure
  • cloud-native virtualized network functions (VNF)
  • DevOps agile development process
  • everything-as-service design paradigm
  • intelligent process automation
  • edge computing
  • cloud infrastructure
  • programing paradigm suitable for building an autonomous system . i.e., teleo-reactive programs, which is a set of reactive rules that continuously sense the environment and trigger actions whose continuous execution eventually leads the system to satisfy a goal. (Nils Nilsson, 1996)
  • open-source solutions

#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks

Sofia  Maggio

Sofia Maggio

1626106680

Neural networks forward propagation deep dive 102

Forward propagation is an important part of neural networks. Its not as hard as it sounds ;-)

This is part 2 in my series on neural networks. You are welcome to start at part 1 or skip to part 5 if you just want the code.

So, to perform gradient descent or cost optimisation, we need to write a cost function which performs:

  1. Forward propagation
  2. Backward propagation
  3. Calculate cost & gradient

In this article, we are dealing with (1) forward propagation.

In figure 1, we can see our network diagram with much of the details removed. We will focus on one unit in level 2 and one unit in level 3. This understanding can then be copied to all units. (ps. one unit is one of the circles below)

Our goal in forward prop is to calculate A1, Z2, A2, Z3 & A3

Just so we can visualise the X features, see figure 2 and for some more info on the data, see part 1.

Initial weights (thetas)

As it turns out, this is quite an important topic for gradient descent. If you have not dealt with gradient descent, then check this article first. We can see above that we need 2 sets of weights. (signified by ø). We often still calls these weights theta and they mean the same thing.

We need one set of thetas for level 2 and a 2nd set for level 3. Each theta is a matrix and is size(L) * size(L-1). Thus for above:

  • Theta1 = 6x4 matrix

  • Theta2 = 7x7 matrix

We have to now guess at which initial thetas should be our starting point. Here, epsilon comes to the rescue and below is the matlab code to easily generate some random small numbers for our initial weights.

function weights = initializeWeights(inSize, outSize)
  epsilon = 0.12;
  weights = rand(outSize, 1 + inSize) * 2 * epsilon - epsilon;
end

After running above function with our sizes for each theta as mentioned above, we will get some good small random initial values as in figure 3

. For figure 1 above, the weights we mention would refer to rows 1 in below matrix’s.

Now, that we have our initial weights, we can go ahead and run gradient descent. However, this needs a cost function to help calculate the cost and gradients as it goes along. Before we can calculate the costs, we need to perform forward propagation to calculate our A1, Z2, A2, Z3 and A3 as per figure 1.

#machine-learning #machine-intelligence #neural-network-algorithm #neural-networks #networks