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Zero Trust Network: Milestone In IT Security Or Just Another Trust Model? What Is Zero Trust Network?
There are several benefits of replacing the traditional system with Zero trust network. In the ecosystem of zero trust network model, it is assumed that we are working in an open environment which has endless threats and vulnerabilities. This makes sure that every data; incoming or outgoing is encrypted to avoid any mishappening! It is a bit inconvenient for users as they have to log in for session as there is no provision of cookies that will keep them logged in. Also, the privileges of administrators are restricted. The admins are no longer permitted to access or use their power anytime, but during their working hours. Moreover, the systems are subdivided to be make sure they are ready to work with zero trust approach. These are divided separately in sections to prevent any other person from accessing the sensitive information.
Also Read : Same Old Cyber Security Failures
Best Practices for Zero Trust Network Model That You Must Start With1. Identify the Most Sensitive Data You Have
You may have to invest a lot of time for this but it will be worth the effort as you’ll get an insight into the data and users who have access to it. You may now take the important steps and practices for securing it.
Well you won’t have to invest your time for this as you’ll be able to track this while carrying out the previous step! But document this to make sure you have a record of the people who have access to it and right to share the same!
3. Come Up with Regulations
As every user in the zero-trust network must know about the basics of what he is allowed to do and what not, this is crucial! Also, in case of any dispute you’ll have a guideline to settle it!
4. Keep Monitoring Continually
Everything will go in vain if your network is left unsupervised! So, make sure you have experts to track and supervise the activities in your network!
5. Get Rid of Toxic Data
In every organization, there is a dataset which is no longer useful but still the personnel’s involved find it hard to let go of it. If you too have the same, dispose it off immediately! It will not only free up some space for you but also make you less vulnerable to hackers.
Must read : Tips to Enhance Privacy on Chrome For Android
Yes, there are several things to upgrade, but switching to this from a traditional network will give a boost to your network security! So, start following zero trust approach for your network and stay shielded! Certainly, this will not make you cent percent secure but reduce the risks considerably! What do you think?
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The Role Of Ai Developers In Building Trust In Indian Healthcare
How AI developers can increase patient trust and transparency in Indian Healthcare
The technology that has captivated multiple sectors, artificial intelligence (AI), is being hailed as a tool that will help provide access to quality medical care for all, including through the development and improvement of diagnostics, personalised medical care, illness prevention, and the discovery of new treatments. The use of AI in medicine is expected to more than tenfold in the next five years. Artificial Intelligence is defined as the use of coded computer software routines (algorithms) with specific instructions to perform tasks that would normally require the use of a human brain. Such software can assist people in understanding and processing language, recognising sounds, identifying objects, and solving problems by utilising learning patterns. Machine learning is a method of constantly improving an algorithm. The refinement process employs large amounts of data and is carried out automatically, allowing the algorithm to change in order to improve the precision of artificial intelligence. Simply put, artificial intelligence (AI) allows computers to model intelligent behavior with minimal human intervention and has been shown to outperform humans in specific tasks.
Deep neural networks (a subset of AI) were used successfully in 2023 to analyze skin cancer images with greater accuracy than a dermatologist and to diagnose diabetic retinopathy (DR) from retinal images. However, the definition of artificial intelligence (AI) is changing. In addition to the more technical definition given above, AI is viewed as something resembling human intelligence, aspiring to outperform the capabilities of any individual technology. It is envisioned as a technological interaction that allows a machine to perform a function that ‘feels’ human. Artificial General Intelligence refers to a machine’s ability to perform any task that a human can perform (AGI). AGI systems are built with the human brain as a model. However, AGI has not yet been achieved; experts recently forecast its emergence by 2060. ML, natural language processing (NLP), speech recognition (text-to-speech and speech-to-text), image recognition and machine vision, expert systems (a computer system that emulates the decision-making ability of a human expert), robotics, and systems for planning, scheduling, and optimization are all examples of artificial intelligence for health.
ML is a key component of AI that allows systems to automatically learn and improve without being explicitly programmed. In fact, AI cannot exist without ML. Computer programmes access and use data to learn without human intervention or assistance, and they adjust actions accordingly. Deep learning (DL), a type of machine learning (ML), is inspired by the human brain and employs multi-layered neural networks to discover complex patterns and relationships in large datasets that traditional ML may miss (Health Nucleus, undated).
In India, health systems face significant challenges in terms of quality, accessibility, affordability, and equity. On the one hand, India has some of the best hospitals in the world, which contributes to the growing medical tourism industry. On the other hand, qualified medical professionals are in short supply: the ratio of available doctors to population (assuming an availability rate of 80%) is estimated to be 1:1,596. (calculated from Central Bureau of Health Intelligence, 2023). In rural areas, the ratio is especially low, forcing patients to travel long distances for even basic care. New ML or other AI technologies could help address a number of these challenges, including improving access to quality healthcare, particularly in rural and low-income areas; addressing the uneven ratio of skilled doctors to patients; improving doctor and nurse training and efficiency, particularly in complex procedures; and enabling the delivery of personalised healthcare at scale.
Do You Trust Google Enough To Use ‘Pay With Google?’
We have completely different ways of paying for things than we did twenty years ago. While we used to use cash or check and sometimes charge for larger purchases, now people often don’t carry money, never carry a checkbook, and usually do everything with plastic or even sometimes with their smartphone.
Online there are more options, and now there is an additional one being added to the mix. Along with using a credit card, you can also use a system such as Paypal or Apple Pay. And now “Pay with Google” has been introduced, working much like Paypal. We asked our writers, “Do you trust Google enough to use ‘Pay with Google?’ ”
Our OpinionNicholas sees that Google has “almost no reason not to be trusted.” Additionally, they’re more capitalized than all the big U.S. banks, so if they offer a payment service, it makes sense on many levels, “from proving trustworthy to having cash backup in case of a ‘bank run,’ “ referencing a group of customers asking for all their money back at the same time, knowing that U.S. banks aren’t in a position to do that.
As an Android user, Damien is already using the Google payment system to buy apps, so there isn’t any reason for him not to trust them on websites. Additionally, “as a publisher, since Pay with Google does not charge a transaction fee, I am already planning on adding it as one of the payment methods for [Make Tech Easier].”
Phil’s only misgiving has nothing to do with finance but everything to do with data. “Google’s data is its business model, not its innovation, science, or services.” Even if they share information about him anonymously, it would still make him uneasy. That being said, he does recognize they are financially stronger than most banks.
Miguel feels that “we give our personally identifiable information too liberally, yet Google has a track record of safeguarding their users’ data from compromise rather efficiently,” so he has mixed feelings. He doesn’t interact with any financial institution for any length of time, withdrawing money from the ATM as soon as he can to pay for things with cash. “If I can avoid using digital finance, I will.” However, when it comes to Google’s payment system, he doesn’t see a reason to not have at least some trust in it. He also agrees with Phil regarding the concern of Google using your purchase history for their own gain.
Ada won’t use it as her sole way of payment, so she’s not that concerned about privacy. She doesn’t care if the world knows she’s buying books, clothes, home appliances, etc. She also sees it as good when a new, solid player is added to the market. However, even though they’re Google, there are already many other players in the field, so she thinks they’ll need some luck in succeeding.
Alex jokes, “For the amount of Google services I use, I better trust them!” He realizes they use personal information to sell things, but notes their products are often the best. They have no legitimate competition when it comes to Google search, and they’ve never given anyone a reason to not trust them. “Whether I use Pay with Google will come down to the quality of the service rather than the trust I place in Google.”
Christopher figures if he’s going to give his financial information to an Internet company, Paypal and Google are by far the best bets. He asks when was the last time we heard of Google’s servers being breached and thinks the biggest security incident with Google was a phishing link with a fake Docs web app. He trusts Google to keep his data safe because “thus far they’ve proven very good at it, and as one of the biggest web service providers on the planet, I have no doubt that doing so has required furious maintenance and upkeep.”
Ryan can see why people would be apprehensive with Pay with Google, but he’s been using his bank’s Android app to pay for things with his phone. “Since Pay with Google allows people to spend their money faster and easier, the only issue I can see is for people who aren’t good at managing their money.”
Personally, I would have no reason for not trusting Google either. I have no reason to use it as an Apple and Paypal user and because the places where I can’t use Apple Pay or Paypal, I use my credit card. The places where I have to input my credit card credentials because I can’t use the others are less likely to be using Pay with Google. But if I was looking for another option, I’d have no problem using Pay with Google.
Your OpinionLaura Tucker
Laura has spent nearly 20 years writing news, reviews, and op-eds, with more than 10 of those years as an editor as well. She has exclusively used Apple products for the past three decades. In addition to writing and editing at MTE, she also runs the site’s sponsored review program.
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Citation Flow And Trust Flow: All That You Want To Know
Have you ever wondered what citation flow and trust flow are? It can be confusing for those who don’t know about them. But understanding their importance is essential when it comes to improving online visibility and presence. Citation Flow and Trust Flow are both metrics that measure the authoritative status of a website or web page by analyzing how many other websites link to them. They can significantly affect your ranking in search engine results as well as guide people towards valuable sources of content. In this blog post, we’ll dive deep into the similarities between Citation Flow vs Trust Flow, explain why they’re important, and provide useful tips on utilizing each metric optimally to boost your overall SEO performance.
What is Citation Flow?Citation flow is a metric used to know how many links are pointing to a site. It helps you determine the quality or influence of that site. This metrics focus on the number of links rather than the quality.
The word “influential” in this context has a unique meaning. It determines how much a blog or site can impact its readers. If more domains point to a page, it considers the page more influential.
The trust flow is also a determining factor for citation flow. An increase in the trust flow is likely to increase the citation flow. However, there is no stringent rule on how the increase in citation flow affects the trust flow.
In simple terms, if Trust flows increase by 10%, that necessarily doesn’t mean the citation flow will increase with the same percentage.
Similarly, no rule says an increase in citation flow will increase the trust flow.
For instance, suppose you got some backlinks from sites with high citation flow. Even with fewer inbound links, you will get a big citation boost in such incidents.
Trust flow is also essential in this matter. Your site will be negatively affected if sites with a high citation but low Trust flows link with you.
What is Trust Flow?Trust flow is a metric to decide the trustworthiness of a site. Your site will be considered more trustworthy if you have more quality backlinks.
You will have a greater trust flow if trustworthy and authoritative backlinks are linked to your site.
You must probably wonder why trust flow numbers are lesser than citation flow.
This is because not every backlink carries the same quality. This means your site may have hundreds or thousands of backlinks. That does not necessarily mean these links are trustworthy. Your site’s trust flow only increases if you receive quality backlinks.
Here is the truth- you may try harder to eliminate bad links from your site. However, you will always get some auto-generated bad links from various directories or unreliable sources.
Hence, the chances of getting high-quality backlinks are always low. As a result, the Trust flow rarely overtakes the citation flow.
Experts believe high trust flow is linked to a site’s organic traffic.
A site with a high trust flow will likely have a high-quality backlink portfolio. This means google boosted the ranking of the site. In other words, a high trust flow makes google and other search engines believe your site has high-quality content.
Trust Flow and Citation Flow RatioThe average Trust to citation flow ratio should not be lower than 0.5. However, 1 remains the most desirable ratio.
The ratio between trust and citation flow determines the overall worthiness of a website.
If a site has a citation flow of 60 and a trust flow of 30, the ratio is 2:1 or 0.5. The maximum ratio usually doesn’t exceed 0.9 in most cases. However, for google, the Trust to citation ratio could look like 98:99.
If the trust flow is significantly less than the citation flow, it clearly means your site has many low-quality backlinks. If that happens, you need to address the matter right away.
You can remove bad links from your site without affecting your ranks. Removing bad links is good for your site. Imagine some visitors coming to your site and seeing inappropriate links or popups from bad links. Bad links give a bad name to your site. Removing these links is the only solution.
How to Measure Flow Metrics?Citation flow and trust flow are flagship metrics created by chúng tôi in 2012. The digital marketing industry now uses these metrics to measure the health of an URL.
To measure these flow metrics, do the following −
Go to the official website of Majestic SEO.
Enter the URL and select fresh index (set at default).
Hit search
The window will display the backlink profile of the URL along with the flow metrics.
Non-signed-up users get limited usage as per the policy. To extend your usage limit, you have to register a free account.
To check multiple URLs, you need to use the bulk backlink checker tool and the raven SEO tools alongside the Majestic SEO API.
Benefits of Majestic SEO
It provides exceptional link quality analysis
It helps identify the reason behind the penguin penalty
It prevents the accumulation of irrelevant links on your site
It assists in identifying top influencers in your niche
It allows content writers and bloggers to find quality content
Tips to Increase Flow Metrics Focus on Trust FlowPay more focus to gain quality links. This will increase your trust flow and maintain a healthy Trust to citation ratio. Increasing your citation flow with low-end links is of no use. Ultimately, quality always wins over quantity.
Aims for Authoritative BacklinksA single authoritative backlink holds much more power than 1000 low-quality backlinks. Plus, you should also remove all unnecessary backlinks to increase your flow ratio. A low flow ratio can hurt your site’s worthiness.
Don’t go Overboard with BacklinksIndeed backlinks are great, but keep them within limits. If your site is new, you should focus more on creating quality content. Then you should focus on building backlinks. A great way to build backlinks is through guest posting. However, you should keep it to a minimum.
Stay within your NicheGetting irrelevant backlinks to your content won’t increase your trust flow. This is because MajesticSEO calculates your topical trust scores. This means to rank higher in google, you need higher trust scores in your niche.
Go for Internal LinkingA well-planned internal linking strategy can increase your trust and citation flow. If you link all the influential pages to your homepage, this could improve your flow metrics. Having backlinks to the homepage and internal pages can significantly appreciate your flow metrics.
Use Trustworthy Backlinks for More Trust ScoreRegarding the quality of content, you need citations and references from authoritative links. For this, TLDs like .gov and .edu are pretty essential. It helps improve your site’s trust score.
ConclusionOverall, Citation Flow and Trust Flow are two very important metrics of Link Analysis. For any website to be successful in terms of gaining organic search engine traffic, it needs to have a strong balance between the two. This means that websites need to have a varied and diverse range of inbound links from sources across the internet in order for the algorithm to recognize their importance. Furthermore, these links should come from high-authority sites as much as possible, as this will increase the overall Trust Flow score of the website. To ensure success with link analysis, webmasters should ensure that all of their incoming links are both varied and from credible sources. With proper understanding of Citation Flow and Trust Flow as outlined here, webmasters can set themselves up for success when it comes to SEO in general.
Build Your First Image Classification Model In Just 10 Minutes!
14 minutes
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Rating: 5 out of 5.
Introduction“Build a deep learning model in a few minutes? It’ll take hours to train! I don’t even have a good enough machine.” I’ve heard this countless times from aspiring data scientists who shy away from building deep learning models on their own machines.
You don’t need to be working for Google or other big tech firms to work on deep learning datasets! It is entirely possible to build your own neural network from the ground up in a matter of minutes without needing to lease out Google’s servers. Fast.ai’s students designed a model on the Imagenet dataset in 18 minutes – and I will showcase something similar in this article.
Deep learning is a vast field so we’ll narrow our focus a bit and take up the challenge of solving an Image Classification project. Additionally, we’ll be using a very simple deep learning architecture to achieve a pretty impressive accuracy score.
You can consider the Python code we’ll see in this article as a benchmark for building Image Classification models. Once you get a good grasp on the concept, go ahead and play around with the code, participate in competitions and climb up the leaderboard!
If you’re new to deep learning and are fascinated by the field of computer vision (who isn’t?!), do check out the ‘Computer Vision using Deep Learning‘ course. It’s a comprehensive introduction to this wonderful field and will set you up for what is inevitably going to a huge job market in the near future.
Project to apply Image Classification
Problem StatementMore than 25% of the entire revenue in E-Commerce is attributed to apparel & accessories. A major problem they face is categorizing these apparels from just the images especially when the categories provided by the brands are inconsistent. This poses an interesting computer vision problem that has caught the eyes of several deep learning researchers.
Fashion MNIST is a drop-in replacement for the very well known, machine learning hello world – MNIST dataset which can be checked out at ‘Identify the digits’ practice problem. Instead of digits, the images show a type of apparel e.g. T-shirt, trousers, bag, etc. The dataset used in this problem was created by Zalando Research.
Practice Now
What Is Image Classification?Consider the below image:
You will have instantly recognized it – it’s a (swanky) car. Take a step back and analyze how you came to this conclusion – you were shown an image and you classified the class it belonged to (a car, in this instance). And that, in a nutshell, is what image classification is all about.
There are potentially n number of categories in which a given image can be classified. Manually checking and classifying images is a very tedious process. The task becomes near impossible when we’re faced with a massive number of images, say 10,000 or even 100,000. How useful would it be if we could automate this entire process and quickly label images per their corresponding class?
Now that we have a handle on our subject matter, let’s dive into how an image classification model is built, what are the prerequisites for it, and how it can be implemented in Python.
Setting Up the Structure of Our Image DataOur data needs to be in a particular format in order to solve an image classification problem. We will see this in action in a couple of sections but just keep these pointers in mind till we get there.
You should have 2 folders, one for the train set and the other for the test set. In the training set, you will have a .csv file and an image folder:
The .csv file contains the names of all the training images and their corresponding true labels
The image folder has all the training images.
The .csv file in our test set is different from the one present in the training set. This test set .csv file contains the names of all the test images, but they do not have any corresponding labels. Can you guess why? Our model will be trained on the images present in the training set and the label predictions will happen on the testing set images
If your data is not in the format described above, you will need to convert it accordingly (otherwise the predictions will be awry and fairly useless).
Breaking Down the Process of Model BuildingBefore we deep dive into the Python code, let’s take a moment to understand how an image classification model is typically designed. We can divide this process broadly into 4 stages. Each stage requires a certain amount of time to execute:
Loading and pre-processing Data – 30% time
Defining Model architecture – 10% time
Training the model – 50% time
Estimation of performance – 10% time
Let me explain each of the above steps in a bit more detail. This section is crucial because not every model is built in the first go. You will need to go back after each iteration, fine-tune your steps, and run it again. Having a solid understanding of the underlying concepts will go a long way in accelerating the entire process.
Stage 1: Loading and pre-processing the dataData is gold as far as deep learning models are concerned. Your image classification model has a far better chance of performing well if you have a good amount of images in the training set. Also, the shape of the data varies according to the architecture/framework that we use.
Hence, the critical data pre-processing step (the eternally important step in any project). I highly recommend going through the ‘Basics of Image Processing in Python’ to understand more about how pre-processing works with image data.
But we are not quite there yet. In order to see how our model performs on unseen data (and before exposing it to the test set), we need to create a validation set. This is done by partitioning the training set data.
In short, we train the model on the training data and validate it on the validation data. Once we are satisfied with the model’s performance on the validation set, we can use it for making predictions on the test data.
Time required for this step: We require around 2-3 minutes for this task.
Stage 2: Defining the model’s architectureThis is another crucial step in our deep learning model building process. We have to define how our model will look and that requires answering questions like:
How many convolutional layers do we want?
What should be the activation function for each layer?
How many hidden units should each layer have?
And many more. These are essentially the hyperparameters of the model which play a MASSIVE part in deciding how good the predictions will be.
How do we decide these values? Excellent question! A good idea is to pick these values based on existing research/studies. Another idea is to keep experimenting with the values until you find the best match but this can be quite a time consuming process.
Time required for this step: It should take around 1 minute to define the architecture of the model.
Stage 3: Training the modelFor training the model, we require:
Training images and their corresponding true labels
Validation images and their corresponding true labels (we use these labels only to validate the model and not during the training phase)
We also define the number of epochs in this step. For starters, we will run the model for 10 epochs (you can change the number of epochs later).
Time required for this step: Since training requires the model to learn structures, we need around 5 minutes to go through this step.
And now time to make predictions!
Stage 4: Estimating the model’s performanceFinally, we load the test data (images) and go through the pre-processing step here as well. We then predict the classes for these images using the trained model.
Time required for this step: ~ 1 minute.
Setting Up the Problem Statement and Understanding the DataWe will be picking up a really cool challenge to understand image classification. We have to build a model that can classify a given set of images according to the apparel (shirt, trousers, shoes, socks, etc.). It’s actually a problem faced by many e-commerce retailers which makes it an even more interesting computer vision problem.
This challenge is called ‘Identify the Apparels’ and is one of the practice problems we have on our DataHack platform. You will have to register and download the dataset from the above link.
We have a total of 70,000 images (28 x 28 dimension), out of which 60,000 are from the training set and 10,000 from the test one. The training images are pre-labelled according to the apparel type with 10 total classes. The test images are, of course, not labelled. The challenge is to identify the type of apparel present in all the test images.
We will build our model on Google Colab since it provides a free GPU to train our models.
Steps to Build Our ModelTime to fire up your Python skills and get your hands dirty. We are finally at the implementation part of our learning!
Setting up Google Colab
Importing Libraries
Loading and Preprocessing Data – (3 mins)
Creating a validation set
Defining the model structure – (1 min)
Training the model – (5 min)
Making predictions – (1 min)
Let’s look at each step in detail.
Step 1: Setting up Google Colab
Since we’re importing our data from a Google Drive link, we’ll need to add a few lines of code in our Google Colab notebook. Create a new Python 3 notebook and write the following code blocks:
!pip install PyDriveThis will install PyDrive. Now we will import a few required libraries:
import os from chúng tôi import GoogleAuth from pydrive.drive import GoogleDrive from google.colab import auth from oauth2client.client import GoogleCredentialsNext, we will create a drive variable to access Google Drive:
auth.authenticate_user() gauth = GoogleAuth() gauth.credentials = GoogleCredentials.get_application_default() drive = GoogleDrive(gauth)To download the dataset, we will use the ID of the file uploaded on Google Drive:
download = drive.CreateFile({'id': '1BZOv422XJvxFUnGh-0xVeSvgFgqVY45q'})Replace the ‘id’ in the above code with the ID of your file. Now we will download this file and unzip it:
download.GetContentFile('train_LbELtWX.zip') !unzip train_LbELtWX.zipYou have to run these code blocks every time you start your notebook.
Step 2 : Import the libraries we’ll need during our model building phase.
import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from keras.utils import to_categorical from tqdm import tqdmStep 3: Recall the pre-processing steps we discussed earlier. We’ll be using them here after loading the data.
train = pd.read_csv('train.csv')Next, we will read all the training images, store them in a list, and finally convert that list into a numpy array.
# We have grayscale images, so while loading the images we will keep grayscale=True, if you have RGB images, you should set grayscale as False train_image = [] for i in tqdm(range(train.shape[0])): img = image.load_img('train/'+train['id'][i].astype('str')+'.png', target_size=(28,28,1), grayscale=True) img = image.img_to_array(img) img = img/255 train_image.append(img) X = np.array(train_image)As it is a multi-class classification problem (10 classes), we will one-hot encode the target variable.
y=train['label'].values y = to_categorical(y)Step 4: Creating a validation set from the training data.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2)Step 5: Define the model structure.
We will create a simple architecture with 2 convolutional layers, one dense hidden layer and an output layer.
model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax'))Next, we will compile the model we’ve created.
Step 6: Training the model.
In this step, we will train the model on the training set images and validate it using, you guessed it, the validation set.
model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test))Step 7: Making predictions!
We’ll initially follow the steps we performed when dealing with the training data. Load the test images and predict their classes using the model.predict_classes() function.
download = drive.CreateFile({'id': '1KuyWGFEpj7Fr2DgBsW8qsWvjqEzfoJBY'}) download.GetContentFile('test_ScVgIM0.zip') !unzip test_ScVgIM0.zipLet’s import the test file:
test = pd.read_csv('test.csv')Now, we will read and store all the test images:
test_image = [] for i in tqdm(range(test.shape[0])): img = image.load_img('test/'+test['id'][i].astype('str')+'.png', target_size=(28,28,1), grayscale=True) img = image.img_to_array(img) img = img/255 test_image.append(img) test = np.array(test_image) # making predictions prediction = model.predict_classes(test)We will also create a submission file to upload on the DataHack platform page (to see how our results fare on the leaderboard).
download = drive.CreateFile({'id': '1z4QXy7WravpSj-S4Cs9Fk8ZNaX-qh5HF'}) download.GetContentFile('sample_submission_I5njJSF.csv') # creating submission file sample = pd.read_csv('sample_submission_I5njJSF.csv') sample['label'] = prediction sample.to_csv('sample_cnn.csv', header=True, index=False)Download this sample_cnn.csv file and upload it on the contest page to generate your results and check your ranking on the leaderboard. This will give you a benchmark solution to get you started with any Image Classification problem!
You can try hyperparameter tuning and regularization techniques to improve your model’s performance further. I ecnourage you to check out this article to understand this fine-tuning step in much more detail – ‘A Comprehensive Tutorial to learn Convolutional Neural Networks from Scratch’.
New Practice ProblemLet’s test our learning on a different dataset. We’ll be cracking the ‘Identify the Digits’ practice problem in this section. Go ahead and download the dataset. Before you proceed further, try to solve this on your own. You already have the tools to solve it – you just need to apply them! Come back here to check your results or if you get stuck at some point.
In this challenge, we need to identify the digit in a given image. We have a total of 70,000 images – 49,000 labelled ones in the training set and the remaining 21,000 in the test set (the test images are unlabelled). We need to identify/predict the class of these unlabelled images.
Ready to begin? Awesome! Create a new Python 3 notebook and run the following code:
# Setting up Colab !pip install PyDrive import os from chúng tôi import GoogleAuth from pydrive.drive import GoogleDrive from google.colab import auth from oauth2client.client import GoogleCredentials auth.authenticate_user() gauth = GoogleAuth() gauth.credentials = GoogleCredentials.get_application_default() drive = GoogleDrive(gauth) # Replace the id and filename in the below codes download = drive.CreateFile({'id': '1ZCzHDAfwgLdQke_GNnHp_4OheRRtNPs-'}) download.GetContentFile('Train_UQcUa52.zip') !unzip Train_UQcUa52.zip # Importing libraries import keras from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.preprocessing import image import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from keras.utils import to_categorical from tqdm import tqdm train = pd.read_csv('train.csv') # Reading the training images train_image = [] for i in tqdm(range(train.shape[0])): img = image.load_img('Images/train/'+train['filename'][i], target_size=(28,28,1), grayscale=True) img = image.img_to_array(img) img = img/255 train_image.append(img) X = np.array(train_image) # Creating the target variable y=train['label'].values y = to_categorical(y) # Creating validation set X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.2) # Define the model structure model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=(28,28,1))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax')) # Compile the model # Training the model model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test)) download = drive.CreateFile({'id': '1zHJR6yiI06ao-UAh_LXZQRIOzBO3sNDq'}) download.GetContentFile('Test_fCbTej3.csv') test_file = pd.read_csv('Test_fCbTej3.csv') test_image = [] for i in tqdm(range(test_file.shape[0])): img = image.load_img('Images/test/'+test_file['filename'][i], target_size=(28,28,1), grayscale=True) img = image.img_to_array(img) img = img/255 test_image.append(img) test = np.array(test_image) prediction = model.predict_classes(test) download = drive.CreateFile({'id': '1nRz5bD7ReGrdinpdFcHVIEyjqtPGPyHx'}) download.GetContentFile('Sample_Submission_lxuyBuB.csv') sample = pd.read_csv('Sample_Submission_lxuyBuB.csv') sample['filename'] = test_file['filename'] sample['label'] = prediction sample.to_csv('sample.csv', header=True, index=False)Submit this file on the practice problem page to get a pretty decent accuracy number. It’s a good start but there’s always scope for improvement. Keep playing around with the hyperparameter values and see if you can improve on our basic model.
ConclusionWho said deep learning models required hours or days to train. My aim here was to showcase that you can come up with a pretty decent deep learning model in double-quick time. You should pick up similar challenges and try to code them from your end as well. There’s nothing like learning by doing!
The top data scientists and analysts have these codes ready before a Hackathon even begins. They use these codes to make early submissions before diving into a detailed analysis. Once they have a benchmark solution, they start improving their model using different techniques.
Frequently Asked Questions Related“Explainability And Trust Are Extremely Important Areas For Ai Co.s” Says Akshaya Bhargava
Artificial intelligence has proven to be a game changer in the business domain in terms of achieving accurate results. But the AI models the businesses are designed around depend heavily on data. Unless the developers have humongous data sets that are relevant to the specific use case, AI models do not prove to be of any significance. What if a new business idea can be conceived based on the existing AI model without having to scout for data? Sounds like a complex proposition, but there are companies like Bridgeweave that help startups, particularly in the fin-tech industry with end-to-end business cycle management to reach their goals with least effort. Analytics Insight has engaged in an exclusive interview with Akshaya Bhargava, Founder & Executive Chairman, of
1. Kindly brief us about the company, its specialization, and the services that your company offers.Bridgeweave is a UK-based fintech firm that uses AI models to provide institutional-quality research signals and investment ideas to investors to make better investment decisions. We make use of sophisticated technology to produce tangible outcomes and we believe that this kind of customer-centric approach will enable autonomous wealth management, transforming the wealth management industry in the future. InvestorAi is an AI-powered personal investment analyst for retail investors, that uses AI algorithms that have been trained for global equity markets. It is based on a subscription model and provides a variety of amazing features. Our ‘Follow the Machine’ portfolios are machine-generated portfolios that rebalance automatically and are open for subscription by individual investors.
2. With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company.The Bridgeweave story started when I was the CEO of Barclays Wealth and Investments. I saw a lot of potential in artificial intelligence and thought the new technology can play an important role in creating a better wealth management model that could help people make better financial decisions through the intelligent use of personalized information. The journey so far has been promising. Following the launch of InvestorAi in India in July last year, we announced it in the UK. Today we have over 23,000+ users and have some impressive logos like IIFL, and Paytm Money as partners. We already had an operation and R&D center in Bangalore and have added a second lab in Hyderabad to incubate and foster new ideas. We have our own FtM (follow the machine) portfolios that are listed on Wealthdesk and Smallcase, making it even easier for investors. We have just launched InvestorAi Crypto, which is one of its kind product, that will transform the experience for anyone who makes crypto investments.
3. What is your biggest USP that differentiates the company from competitors?Our USP is to help our customers in making better investment decisions based on predictive signals along with sophisticated and high-quality information.
4. Please brief us about the products and services you provide to your customers and how they get value out of it.Ans: We offer the below-listed products InvestorAi is a uniquely personalized product built using proprietary AI tech that gives insights and signals for equity investors. Our Follow the Machine (FTM) portfolio (listed on Wealthdesk and Smallcase) provide machine-driven investment ideas and auto-rebalancing of assets for our investors. We recently launched the InvestorAi Crypto app that allows users to make investments in crypto strategies and execute them automatically.
5. Tell us how your company is contributing to the artificial intelligence industry of the nation and how the company is benefiting the clients.Ans: AI is powerful technology but for a large part, it has stayed within operations, back office, and risk management as far as the financial services industry is concerned. We are one of the early companies bringing AI into the front office in a way that directly impacts user capabilities and experience. We believe that more such companies need to provide similar services for AI to become ubiquitous and customer friendly.
6. How is artificial intelligence evolving today in the industry as a whole? What are the most important AI trends that you see emerging across the globe?InvestorAi started off as simple machine learning models that used stochastic modeling. Newer techniques like computer vision (that we use in all our products) have brought deep learning to the forefront. However, the downside of deep learning is that it is not very explainable. This needs to change because unless we are able to explain how the machine has arrived at a certain conclusion, it will never be fully trusted. We believe that explainability and trust are two extremely important areas for AI companies like us to focus on.
7. How are disruptive technologies like artificial intelligence impacting today’s innovation?We believe that no technology is inherently disruptive. It is only when human imagination comes up with a new use for that technology to solve a problem or to remove customer friction that the technology use becomes disruptive. In the same way, we do not see AI as a disruptive technology. However, there will always be innovative companies who use technology cleverly to solve customer problems or to come up with new products.
8. What are your growth plans for the next 12 months?Artificial intelligence has proven to be a game changer in the business domain in terms of achieving accurate results. But the AI models the businesses are designed around depend heavily on data. Unless the developers have humongous data sets that are relevant to the specific use case, AI models do not prove to be of any significance. What if a new business idea can be conceived based on the existing AI model without having to scout for data? Sounds like a complex proposition, but there are companies like Bridgeweave that help startups, particularly in the fin-tech industry with end-to-end business cycle management to reach their goals with least effort. Analytics Insight has engaged in an exclusive interview with Akshaya Bhargava, Founder & Executive Chairman, of Bridgeweave Bridgeweave is a UK-based fintech firm that uses AI models to provide institutional-quality research signals and investment ideas to investors to make better investment decisions. We make use of sophisticated technology to produce tangible outcomes and we believe that this kind of customer-centric approach will enable autonomous wealth management, transforming the wealth management industry in the future. InvestorAi is an AI-powered personal investment analyst for retail investors, that uses AI algorithms that have been trained for global equity markets. It is based on a subscription model and provides a variety of amazing features. Our ‘Follow the Machine’ portfolios are machine-generated portfolios that rebalance automatically and are open for subscription by individual chúng tôi Bridgeweave story started when I was the CEO of Barclays Wealth and Investments. I saw a lot of potential in artificial intelligence and thought the new technology can play an important role in creating a better wealth management model that could help people make better financial decisions through the intelligent use of personalized information. The journey so far has been promising. Following the launch of InvestorAi in India in July last year, we announced it in the UK. Today we have over 23,000+ users and have some impressive logos like IIFL, and Paytm Money as partners. We already had an operation and R&D center in Bangalore and have added a second lab in Hyderabad to incubate and foster new ideas. We have our own FtM (follow the machine) portfolios that are listed on Wealthdesk and Smallcase, making it even easier for investors. We have just launched InvestorAi Crypto, which is one of its kind product, that will transform the experience for anyone who makes crypto chúng tôi USP is to help our customers in making better investment decisions based on predictive signals along with sophisticated and high-quality chúng tôi offer the below-listed productsis a uniquely personalized product built using proprietary AI tech that gives insights and signals for equity investors. Ourportfolio (listed on Wealthdesk and Smallcase) provide machine-driven investment ideas and auto-rebalancing of assets for our investors. We recently launched theapp that allows users to make investments in crypto strategies and execute them chúng tôi is powerful technology but for a large part, it has stayed within operations, back office, and risk management as far as the financial services industry is concerned. We are one of the early companies bringing AI into the front office in a way that directly impacts user capabilities and experience. We believe that more such companies need to provide similar services for AI to become ubiquitous and customer friendly.InvestorAi started off as simple machine learning models that used stochastic modeling. Newer techniques like computer vision (that we use in all our products) have brought deep learning to the forefront. However, the downside of deep learning is that it is not very explainable. This needs to change because unless we are able to explain how the machine has arrived at a certain conclusion, it will never be fully trusted. We believe that explainability and trust are two extremely important areas for AI companies like us to focus chúng tôi believe that no technology is inherently disruptive. It is only when human imagination comes up with a new use for that technology to solve a problem or to remove customer friction that the technology use becomes disruptive. In the same way, we do not see AI as a disruptive technology. However, there will always be innovative companies who use technology cleverly to solve customer problems or to come up with new chúng tôi year is important for us as we will have a new product and new customers using our products and solutions in India and as well as globally. We have launched a product for investors and asset managers for the crypto markets, to make their crypto investing easy.
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