Mya  Lynch

Mya Lynch

1599095520

Complete Guide to Adam Optimization

In the 1940s, mathematical programming was synonymous with optimization. An optimization problem included an objective function that is to be maximized or minimized by choosing input values from an allowed set of values [1].

Nowadays, optimization is a very familiar term in AI. Specifically, in Deep Learning problems. And one of the most recommended optimization algorithms for Deep Learning problems is Adam.

Disclaimer: basic understanding of neural network optimization. Such as Gradient Descent and Stochastic Gradient Descent is preferred before reading.

In this post, I will highlight the following points:

  1. Definition of Adam Optimization
  2. The Road to Adam
  3. The Adam Algorithm for Stochastic Optimization
  4. Visual Comparison Between Adam and Other Optimizers
  5. Implementation
  6. Advantages and Disadvantages of Adam
  7. Conclusion and Further Reading
  8. References

1. Definition of Adam Optimization

The Adam algorithm was first introduced in the paper Adam: A Method for Stochastic Optimization [2] by Diederik P. Kingma and Jimmy Ba. Adam is defined as “a method for efficient stochastic optimization that only requires first-order gradients with little memory requirement” [2]. Okay, let’s breakdown this definition into two parts.

First, stochastic optimization is the process of optimizing an objective function in the presence of randomness. To understand this better let’s think of Stochastic Gradient Descent (SGD). SGD is a great optimizer when we have a lot of data and parameters. Because at each step SGD calculates an estimate of the gradient from a random subset of that data (mini-batch). Unlike Gradient Descent which considers the entire dataset at each step.

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#machine-learning #deep-learning #optimization #adam-optimizer #optimization-algorithms

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Complete Guide to Adam Optimization
Mya  Lynch

Mya Lynch

1599095520

Complete Guide to Adam Optimization

In the 1940s, mathematical programming was synonymous with optimization. An optimization problem included an objective function that is to be maximized or minimized by choosing input values from an allowed set of values [1].

Nowadays, optimization is a very familiar term in AI. Specifically, in Deep Learning problems. And one of the most recommended optimization algorithms for Deep Learning problems is Adam.

Disclaimer: basic understanding of neural network optimization. Such as Gradient Descent and Stochastic Gradient Descent is preferred before reading.

In this post, I will highlight the following points:

  1. Definition of Adam Optimization
  2. The Road to Adam
  3. The Adam Algorithm for Stochastic Optimization
  4. Visual Comparison Between Adam and Other Optimizers
  5. Implementation
  6. Advantages and Disadvantages of Adam
  7. Conclusion and Further Reading
  8. References

1. Definition of Adam Optimization

The Adam algorithm was first introduced in the paper Adam: A Method for Stochastic Optimization [2] by Diederik P. Kingma and Jimmy Ba. Adam is defined as “a method for efficient stochastic optimization that only requires first-order gradients with little memory requirement” [2]. Okay, let’s breakdown this definition into two parts.

First, stochastic optimization is the process of optimizing an objective function in the presence of randomness. To understand this better let’s think of Stochastic Gradient Descent (SGD). SGD is a great optimizer when we have a lot of data and parameters. Because at each step SGD calculates an estimate of the gradient from a random subset of that data (mini-batch). Unlike Gradient Descent which considers the entire dataset at each step.

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#machine-learning #deep-learning #optimization #adam-optimizer #optimization-algorithms

A Complete Guide to the Stages of Penetration Testing

Different Stages of Penetration Tests

The typical penetration testing is broken out in various phases, alike the cyberattack lifecycle. Every single phase has a goal that they require to achieve to further the attack.

  1. Gathering of Crucial Information
  2. Enumeration & Identification
  3. Vulnerability Scanning
  4. Determines the best method of attack
  5. Penetration as well as Exploitation
  6. Risk Analysis as well as Recommendations
  7. Report Preparation (Goals)

#testing #penetration #penetration testing guide #a complete guide

Lisa joly

Lisa joly

1624089840

Big Data Resume: Complete Guide & Samples [2021]

Thanks to the rapidly piling amounts of Big Data, the job profile of a Big Data Engineer is peaking.

In recent years, there has been such unprecedented growth in the demand for Big Data Engineers that it has become one of the top-ranking jobs in Data Science today. Since numerous companies across different industries are hiring Big Data Engineers, there’s never been a better time than now to build a career in Big Data. However, you must know how to present yourself as different from the others; you need to stand out from the crowd. Read the blog to have a better understanding of the scope of Big Data in India.

And how will you do that?

By designing and crafting a detailed, well-structured, and eye-catching Big Data resume!

When applying for a Big Data job, or rather for the post of a Big Data Engineer, your resume is the first point of contact between you and your potential employer. If your resume impresses an employer, you will be summoned for a personal interview. So, the key is to make sure you have a fantastic resume that can get you job interview calls.

Usually, Hiring Managers have to look at hundreds of resumes, be it for any job profile. However, when it comes to high-profile jobs like that of the Big Data Engineer, you must be able to grab the attention of the Hiring Manager by highlighting your skills, qualifications, certifications, and your willingness to upskill.

Let’s begin the resume-building process with the job description and key roles and responsibilities of a Big Data Engineer.

Table of Contents

#big data #big data resume: complete guide & samples #big data resume #big data resume #data science resume #guide

Kennith  Kuhic

Kennith Kuhic

1624642980

The Hitchhiker’s Guide to Optimization in Machine Learning

The aim of this article is to establish a proper understanding of what exactly “optimizing” a Machine Learning algorithm means. Further, we’ll have a look at the gradient-based class (Gradient Descent, Stochastic Gradient Descent, etc.) of optimization algorithms.

_NOTE: _For the sake of simplicity and better understanding, we‘ll restrict the scope of our discussion to supervised machine learning algorithms only.

Machine Learning is the ideal culmination of Applied Mathematics and Computer Science, where we train and use data-driven applications to run inferences on the available data. Generally speaking, for an ML task, the type of inference (i.e., the prediction that the model makes) varies on the basis of the problem statement and the type of data one is dealing with for the task at hand. However, in contrast to these dissimilarities, these algorithms tend to share some similarities as well, especially in the essence of how they operate.

Let’s try to understand the previous paragraph. Consider supervised ML algorithms as a superset. Now, we can go ahead and further divide this superset into smaller sub-groups based on the characteristics these algorithms share:

  • Regression vs classification algorithms
  • Parametric vs non-parametric algorithms
  • Probabilistic vs non-probabilistic algorithms, etc.

Although setting these differences apart, if we observe the generalized representation of a supervised machine learning algorithm, it’s evident that these algorithms tend to work more or less in the same manner.

  • Firstly, we have some labeled data, which can be broken down into the feature set X, and the corresponding label set Y.
  • Then we have the model function, denoted by F, which is a mathematical function that maps the input feature set X_i t the output ŷ_i.

To put it in layman’s terms, every supervised ML algorithm involves passing as input to the model function F a feature set X_i, which the function F processes to generate an output ŷ_i.

However, this is just the inference (or testing) phase of a model, where theoritically, we are supposed to use the model to generate predictions on the data it has never seen before.

But what about “training” the model? Let’s have a look at it next.

#optimization #deep-learning #data-science #artificial-intelligence #machine-learning #optimization

Rylan  Becker

Rylan Becker

1624496700

Optimize Your Algorithms Tail Call Optimization

While writing code and algorithms you should consider tail call optimization (TCO).

What is tail call optimization?

The tail call optimization is the fact of optimizing the recursive functions in order to avoid building up a tall call stack. You should as well know that some programming languages are doing tail call optimizations.

For example, Python and Java decided to don’t use TCO. While JavaScript allows to use TCO since ES2015-ES6.

Even if you know that your favorite language support natively TCO or not, I would definitely recommend you to assume that your compiler/interpreter will not do the work for you.

How to do a tail call optimization?

There are two famous methods to do a tail call optimization and avoid tall call stacks.

1. Going bottom-up

As you know recursions are building up the call stack so if we avoid such recursions in our algorithms it will will allow us to save on the memory usage. This strategy is called the bottom-up (we start from the beginning, while a recursive algorithm starts from the end after building a stack and works backwards.)

Let’s take an example with the following code (top-down — recursive code):

function product1ToN(n) {
  return (n > 1) ? (n * product1ToN(n-1)) : 1;
}

As you can see this code has a problem: it builds up a call stack of size O(n), which makes our total memory cost O(n). This code makes us vulnerable to a stack overflow error, where the call stack gets too big and runs out of space.

In order to optimize our example we need to go bottom-down and remove the recursion:

function product1ToN(n) {
  let result = 1;
  for (let num = 1; num <= n; num++) {
    result *= num;
  }
  return result;
}

This time we are not stacking up our calls in the call stack, and we do use a O(1) space complexity(with a O(n) time complexity).

#memoization #programming #algorithms #optimization #optimize