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The age of AI is upon us and many companies begin to start their AI journey and reap the full potential of AI in their respective industries. But, some still consider AI as an immature technology with plenty of ways for it to go wrong. Therefore, before starting your long AI journey, there are some pitfalls you should avoid in implementing and developing AI solutions. They’re a result of the anecdotal, personal and published experience of AI projects that could have gone better.

1. Building AI systems that have become industry standards

Reinventing the wheel, that’s the reasonable words to describe building an AI system that has become an industry standard. It is a waste of your company’s time and resources. Instead, buy it from a company that has done research and development for years, and has launched a product that has been used and trusted by ample of users. Embrace their solution because this buy decision can get you high-quality AI services at a fraction of the cost and time that it would take to develop in-house. Because building an AI system in-house is a costly and risky endeavor, only do it if the AI system is quite specialized to your business and allow you to build a unique defensible advantage, something that can differentiate your company from its competitors. …


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Photo by Alina Grubnyak on Unsplash

Understanding various convolutional neural network (CNN) architectures can be tough, given the vast amount of information available out there. This article summarizes all relevant Medium articles needed to comprehend these CNN architectures. Before that, a mind map of CNN architecture might help you understanding the helicopter view of the subject.

The Mind Map of CNN Architecture


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Photo by Fikri Rasyid on Unsplash

Let’s admit it, grocery shopping sucks. The crowds, the out of stock items, and the cashier payment queue make grocery shopping painful. No wonder, only 15 percent of customers say that they enjoy grocery shopping. The grudge held by customers towards grocery shopping doesn’t help the retailers, who have been troubled with a high fixed cost, low-profit margin, and intense competition for years. Although the aforementioned problems have bothered the grocery retails and their customers for a long time, there seems to be no improvement in how people doing groceries over the years. Do you remember how you shopped grocery 5, 10, or even 20 years ago? Compare it with how you shop your groceries in the present time. …


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Photo by VanveenJF on Unsplash

Some might say artificial intelligence (AI) is the biggest buzzword of this decade. But, I can say AI is real, revolutionary, and has disrupted many industries. And I believe, its applications have only scratched the surface, with wider applications coming in the near future. Banking and finance, retail and e-commerce, healthcare, and logistics are a handful of industries that have tasted the benefits of using AI in the business.

While many industries have been revolutionized by AI, the application of AI in the oil and gas (O&G) industry has been limited. One of the factors that cause slow adoptions of AI is the unwillingness of O&G industry players to share and open their operational data. Sharing these data is considered as taboo and unthinkable for most of O&G companies because they think these data are sensitive and proprietary. But, data is the core of AI. …


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Photo by Caspar Camille Rubin on Unsplash

The easiest way to make TensorFlow Object Detection API work on NVIDIA RTX 20 Super Series is by using NVIDIA GPU-Accelerated Container (NGC). NGC offers a comprehensive catalog of GPU-accelerated software for deep learning and machine learning frameworks.

Requirements:

  • NVIDIA GPU RTX 20 Super Series
  • Docker and NVIDIA Container Toolkit installed
  • Ubuntu 18.04

Step 1: Install NVIDIA Graphic Driver, CUDA, and cuDNN

We need to make sure to install compatible driver, CUDA, and cuDNN. NVIDIA GPU RTX 20 Super Series is powered by Turing architecture, which is only supported by NVIDIA driver version ≥ 410.48 and CUDA version ≥ 10.0.x. …


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Photo by Markus Spiske on Unsplash

In this tutorial, we will learn about how to modify Pandas dataframes. Three operations are discussed in this tutorial:

  • deleting index and columns
  • renaming index and columns
  • reindexing

First, we need to import the required libraries.

# Importing NumPy module and aliasing as np
import numpy as np
# Importing Pandas module and aliasing as pd
import pandas as pd

1.0. Deleting index and columns

We can delete particular index or columns by calling drop() function. The official documentation of drop() function can be seen here. We can pass inplace = True to delete the data in place.


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Photo by Markus Spiske on Unsplash

Importing data is a crucial step before we can start data exploration and analysis. One of the most commonly used data types is CSV (Comma-Separated Value). In this tutorial, we will learn how to import CSV files into Pandas dataframes.

We will apply the read_csv() function to import CSV files. The complete documentation of read_csv() function can be found here. First, we need to import the required libraries.

# Importing NumPy module and aliasing as np
import numpy as np
# Importing Pandas module and aliasing as pd
import pandas as pd

1.0. Importing CSV files

The most important thing before importing your data is to know the location of your data. It is possible to import data from both online and local files. …


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Photo by Mika Baumeister on Unsplash

Before performing data analysis, we often need to know the structure of our data. In this tutorial, we focus on getting to know how to explore the structure of a dataframe in Pandas. This includes:

  • viewing a small portion of a dataframe
  • getting the shape (i.e., number of rows and columns) of a dataframe
  • getting the index and columns of a dataframe
  • getting the data types of a dataframe

Each will be discussed in the following sections. First, we need to import the required libraries.

#Importing NumPy module and aliasing as np
import numpy as np
#Importing Pandas module and aliasing as pd
import pandas as…


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Photo by Markus Spiske on Unsplash

We often need to select and get subsets of the dataset for performing certain analyses and visualizations. Pandas facilitates data selecting and indexing using three types of multi-axis indexing:

  • indexing operator [] and attribute operator .
  • label-based indexing using .loc[]
  • integer position-based indexing using .iloc[]

Each will be discussed in the following sections. First, we need to import the required libraries.

# Importing NumPy module and aliasing as np
import numpy as np
# Importing Pandas module and aliasing as pd
import pandas as pd

1.0. Indexing and attribute operator

Suppose we have a dataframe as shown below:

In [1]:# Creating 'df' dataframe
df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
columns = ['A', 'B'…


In recent years, the term ‘machine learning’ becomes more than a buzzword. It changes the way we conduct business and make decision every day. In fact, machine learning has been used in numerous applications, such as:

  • Spam filter in your email application (to classify spam/non-spam email)
  • Netflix recommendation engine (to classify movies that you like/do not like)
  • Your phone face recognition feature to unlock the phone

As machine learning is becoming more and more valuable in our daily life, it is important for us to understand what it is and how it works. But, machine learning seems to be highly technical and daunting for noncomputer science people. …

About

Andika Rachman

PhD in Applied AI | Computer Vision & Machine Learning Engineer

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