MNIST stands for ‘Modified National Institute of Standards and Technology database’. It is a database consisting of handwritten digits. The objective is to identify the correct number. This project is straightforward; it should familiarize you with your deep learning framework and teach you how to build and train your first Artificial Neural Network. It also shows you how to solve multiclass classification problems rather than just binary ones.
CIFAR-10 stands for ‘Canadian Institute For Advanced Research’. This dataset comprises 60000 colour images of size 32x32 in ten classes, each with 6000 photos. This project is similar to the previous, although it is a little more complex. It includes colour photos of aeroplanes, birds, dogs, and other objects from ten different classes. It’s a little more challenging to come up with a good classification model here. Instead of utilising a simple neural network, you should use a Convolutional Neural Network and discover how it works.
The task of finding objects of interest within an image and determining which category they belong to is known as image recognition. We naturally recognize items as separate instances and associate them with specific definitions when we view them visually. Visual recognition, on the other hand, is a challenging assignment for machines to do.
In the realm of computer vision, image recognition using artificial intelligence has been a long-standing research challenge. While numerous methods have evolved, image recognition’s unifying purpose is to classify observed objects into multiple categories. Hence, they are also called object recognition.
For decades, the disease diagnosis process has remained the same: a clinician would examine symptoms, conduct lab testing, and consult medical diagnostic standards. Recent breakthroughs in AI/machine learning/deep learning, on the other hand, have enabled computers to diagnose and identify diseases with human precision. The Deep Learning technology is successfully used in the diagnosis of the following:
1. Cancer prognosis and detection.
2. Organ Failure.
3. Autistic disorder.
4. Diabetic retinopathy disease diagnosis.
5. Psoriasis disease diagnosis.
6. Alzheimer’s disease.
7. Parkinson’s disease.
This project falls under the category of Natural Language Processing. Another subset of NLP is Sentiment Analysis, where we find the sentiment of the given text. Text classification is a machine learning technique for categorizing open-ended text into a collection of predetermined categories. Text classifiers can organize, arrange, and organize almost any type of text, including documents, medical research, and files, as well as text found on the internet.
Unstructured data accounts for over 80% of all data, with text being one of the most common categories. Because analyzing, comprehending, organizing, and sifting through text data is difficult and time-consuming due to its messy nature, most businesses do not exploit it to its full potential.
Text classifiers enable businesses to rapidly and cost-effectively arrange various forms of relevant text, including email messages, legal documentation, media platforms, chatbots, surveys, and more. This has allowed companies to save time studying text data, automate business processes, and make data-driven business choices.
Major music and video streaming services such as Spotify and Netflix use Deep learning models to curate a playlist/ watchlist for you. Using data obtained from their interactions, such as impressions, clicks, likes, and purchases, recommender systems are trained to comprehend individuals’ preferences, previous decisions, and characteristics.
Recommender systems aid in the reduction of information overload by assisting consumers in locating relevant items from a large number of options by delivering customized content. Since their ability to predict customer interests and desires on a highly customized level, recommender systems are a favorite of content and product suppliers because they push consumers to just about any product or service that interests them, from books to movies.
Time series forecasting is a critical application of machine learning. While the time component provides more information, time-series issues are more difficult to anticipate than many other tasks. As the name implies, time-series data differ from different data types in that the temporal aspect is significant. On the plus side, this provides us with more information that we can use when creating our machine learning model. The input features and the changes in input/output over time include helpful information.
Through human-to-human dialogue, a deep learning chatbot learns everything from data. The more data you feed it, the more effective it will become at learning. Chatbots will enhance their accuracy if they are trained extensively. Retrieval-based and generative deep learning chatbots are the two primary forms. Retrieval-based chatbots have a ‘repository’ of responses to questions, whereas generative chatbots don’t.
Existing interactions between customers and support workers can train deep learning chatbots, which should be as thorough and varied as feasible. Data reshaping (generating message-response combinations that the machine will recognise) and pre-processing are also part of the training process (adding grammar so that the chatbot can understand errors correctly).
A subset of computer vision, object detection is an automated method for detecting necessary details in an image with respect to the background.
Placing a tight bounding box around these things and linking the relevant object category with each bounding box is the key to solving the object detection challenge. Deep learning, like other computer vision tasks, is the most advanced way of detecting objects.
The number of things in the foreground can change across photos, which complicates object recognition. Consider restricting the object detection problem by assuming that each image has only one thing to understand better how it works. When there is only one object per image, determine a bounding box and categorize the object. Because the bounding box comprises four values, knowing its position makes it a regression issue. The object is then classified, which is a classification problem.
The convolutional neural network (CNN) solves the regression and classification difficulties for our constrained object detection task. Unlike other traditional computer vision tasks like image recognition, key-point detection, and semantic segmentation, our constrained object identification issue has a set number of targets. Modelling the targets as a fixed number of classification or regression tasks can be used to fit them.
Neural style transfer is a technique for blending two images—a content image and a style reference image (such as a famous painter’s work)—so that the output image appears like the content image but is ‘painted’ in the manner of the style reference image.
This is accomplished by adjusting the output image’s content statistics to match the content image’s content statistics and the style reference image’s style statistics. A convolutional network extracts these data from the pictures.
In this blog, you will get to know about “Top 10 Deep Learning Projects for Beginners” For more such information, visit Learnbay.co.
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
Also Read: Why Deep Learning DevCon Comes At The Right Time
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
Read an interview with Dipanjan Sarkar.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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Android Projects with Source Code – Your entry pass into the world of Android
Hello Everyone, welcome to this article, which is going to be really important to all those who’re in dilemma for their projects and the project submissions. This article is also going to help you if you’re an enthusiast looking forward to explore and enhance your Android skills. The reason is that we’re here to provide you the best ideas of Android Project with source code that you can choose as per your choice.
These project ideas are simple suggestions to help you deal with the difficulty of choosing the correct projects. In this article, we’ll see the project ideas from beginners level and later we’ll move on to intermediate to advance.
Before working on real-time projects, it is recommended to create a sample hello world project in android studio and get a flavor of project creation as well as execution: Create your first android project
Android Project: A calculator will be an easy application if you have just learned Android and coding for Java. This Application will simply take the input values and the operation to be performed from the users. After taking the input it’ll return the results to them on the screen. This is a really easy application and doesn’t need use of any particular package.
To make a calculator you’d need Android IDE, Kotlin/Java for coding, and for layout of your application, you’d need XML or JSON. For this, coding would be the same as that in any language, but in the form of an application. Not to forget creating a calculator initially will increase your logical thinking.
Once the user installs the calculator, they’re ready to use it even without the internet. They’ll enter the values, and the application will show them the value after performing the given operations on the entered operands.
Source Code: Simple Calculator Project
Android Project: This is a good project for beginners. A Reminder App can help you set reminders for different events that you have throughout the day. It’ll help you stay updated with all your tasks for the day. It can be useful for all those who are not so good at organizing their plans and forget easily. This would be a simple application just whose task would be just to remind you of something at a particular time.
To make a Reminder App you need to code in Kotlin/Java and design the layout using XML or JSON. For the functionality of the app, you’d need to make use of AlarmManager Class and Notifications in Android.
In this, the user would be able to set reminders and time in the application. Users can schedule reminders that would remind them to drink water again and again throughout the day. Or to remind them of their medications.
Android Project: Another beginner’s level project Idea can be a Quiz Application in android. Here you can provide the users with Quiz on various general knowledge topics. These practices will ensure that you’re able to set the layouts properly and slowly increase your pace of learning the Android application development. In this you’ll learn to use various Layout components at the same time understanding them better.
To make a quiz application you’ll need to code in Java and set layouts using xml or java whichever you prefer. You can also use JSON for the layouts whichever preferable.
In the app, questions would be asked and answers would be shown as multiple choices. The user selects the answer and gets shown on the screen if the answers are correct. In the end the final marks would be shown to the users.
Android Project: Tic-Tac-Toe is a nice game, I guess most of you all are well aware of it. This will be a game for two players. In this android game, users would be putting X and O in the given 9 parts of a box one by one. The first player to arrange X or O in an adjacent line of three wins.
To build this game, you’d need Java and XML for Android Studio. And simply apply the logic on that. This game will have a set of three matches. So, it’ll also have a scoreboard. This scoreboard will show the final result at the end of one complete set.
Upon entering the game they’ll enter their names. And that’s when the game begins. They’ll touch one of the empty boxes present there and get their turn one by one. At the end of the game, there would be a winner declared.
Source Code: Tic Tac Toe Game Project
Android Project: A stopwatch is another simple android project idea that will work the same as a normal handheld timepiece that measures the time elapsed between its activation and deactivation. This application will have three buttons that are: start, stop, and hold.
This application would need to use Java and XML. For this application, we need to set the timer properly as it is initially set to milliseconds, and that should be converted to minutes and then hours properly. The users can use this application and all they’d need to do is, start the stopwatch and then stop it when they are done. They can also pause the timer and continue it again when they like.
Android Project: This is another very simple project idea for you as a beginner. This application as the name suggests will be a To-Do list holding app. It’ll store the users schedules and their upcoming meetings or events. In this application, users will be enabled to write their important notes as well. To make it safe, provide a login page before the user can access it.
So, this app will have a login page, sign-up page, logout system, and the area to write their tasks, events, or important notes. You can build it in android studio using Java and XML at ease. Using XML you can build the user interface as user-friendly as you can. And to store the users’ data, you can use SQLite enabling the users to even delete the data permanently.
Now for users, they will sign up and get access to the write section. Here the users can note down the things and store them permanently. Users can also alter the data or delete them. Finally, they can logout and also, login again and again whenever they like.
Android Project: This app is aimed at the conversion of Roman numbers to their significant decimal number. It’ll help to check the meaning of the roman numbers. Moreover, it will be easy to develop and will help you get your hands on coding and Android.
You need to use Android Studio, Java for coding and XML for interface. The application will take input from the users and convert them to decimal. Once it converts the Roman no. into decimal, it will show the results on the screen.
The users are supposed to just enter the Roman Number and they’ll get the decimal values on the screen. This can be a good android project for final year students.
Android Project: Well, coming to this part that is Virtual Dice or a random no. generator. It is another simple but interesting app for computer science students. The only task that it would need to do would be to generate a number randomly. This can help people who’re often confused between two or more things.
Using a simple random number generator you can actually create something as good as this. All you’d need to do is get you hands-on OnClick listeners. And a good layout would be cherry on the cake.
The user’s task would be to set the range of the numbers and then click on the roll button. And the app will show them a randomly generated number. Isn’t it interesting ? Try soon!
Android Project: This application is very important for you as a beginner as it will let you use your logical thinking and improve your programming skills. This is a scientific calculator that will help the users to do various calculations at ease.
To make this application you’d need to use Android Studio. Here you’d need to use arithmetic logics for the calculations. The user would need to give input to the application that will be in terms of numbers. After that, the user will give the operator as an input. Then the Application will calculate and generate the result on the user screen.
Android Project: An SMS app is another easy but effective idea. It will let you send the SMS to various no. just in the same way as you use the default messaging application in your phone. This project will help you with better understanding of SMSManager in Android.
For this application, you would need to implement Java class SMSManager in Android. For the Layout you can use XML or JSON. Implementing SMSManager into the app is an easy task, so you would love this.
The user would be provided with the facility to text to whichever number they wish also, they’d be able to choose the numbers from the contact list. Another thing would be the Textbox, where they’ll enter their message. Once the message is entered they can happily click on the send button.
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Project walkthrough on Convolution neural networks using transfer learning
From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects. Let’s start implementing or in other words learning.
Take an image as input and return a corresponding dog breed from 133 dog breed categories. If a dog is detected in the image, it will provide an estimate of the dog’s breed. If a human is detected, it will give an estimate of the dog breed that is most resembling the human face. If there’s no human or dog present in the image, we simply print an error.
Let’s break this problem into steps
For all these steps, we use pre-trained models.
Pre-trained models are saved models that were trained on a huge image-classification task such as Imagenet. If these datasets are huge and generalized enough, the saved weights can be used for multiple image detection task to get a high accuracy quickly.
For detecting humans, OpenCV provides many pre-trained face detectors. We use OpenCV’s implementation of Haar feature-based cascade classifiers to detect human faces in images.
### returns "True" if face is detected in image stored at img_path def face_detector(img_path): img = cv2.imread(img_path) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray) return len(faces) > 0
For detecting dogs, we use a pre-trained ResNet-50 model to detect dogs in images, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks.
from keras.applications.resnet50 import ResNet50 ### define ResNet50 model ResNet50_model_detector = ResNet50(weights='imagenet') ### returns "True" if a dog is detected def dog_detector(img_path): prediction = ResNet50_predict_labels(img_path) return ((prediction <= 268) & (prediction >= 151))
For classifying Dog breeds, we use transfer learning
Transfer learning involves taking a pre-trained neural network and adapting the neural network to a new, different data set.
To illustrate the power of transfer learning. Initially, we will train a simple CNN with the following architecture:
Train it for 20 epochs, and it gives a test accuracy of just 3% which is better than a random guess from 133 categories. But with more epochs, we can increase accuracy, but it takes up a lot of training time.
To reduce training time without sacrificing accuracy, we will train the CNN model using transfer learning.
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Learning Python can be difficult. You can spend time reading a textbook or watching videos, but then struggle to actually put what you’ve learned into practice. Or you might spend a ton of time learning syntax and get bored or lose motivation.
How can you increase your chances of success? By building Python projects. That way you’re learning by actually doing what you want to do!
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