It is the age of machine intelligence when trained computers are taking over the tasks traditionally performed by humans. As a result, terms like artificial intelligence, machine learning, and deep learning have come to the limelight and are hot topics in the tech industry.
After all, why not?
Artificial intelligence (AI) has brought about an unprecedented level of advancement in the way we use technology. However, AI is just the broader term and it all comes down to two concepts – machine learning and deep learning.
So, if you’re interested in taking a machine learning or deep learning course, let’s get you familiar with some beginner-level project ideas involving the concepts of deep learning. But first, take a look at how the global AI market is flourishing.
Growing Deep Learning Popularity
Artificial intelligence is the science of making intelligent machines – machines that can ‘think’ and ‘act’ like a human brain after receiving instructions in the form of algorithms. A branch of AI is machine learning, where applications are ‘trained’ to learn from data over time so that more accurate predictions and decisions can be made from new data. A subset of machine learning is deep learning, which simulates the structure and function of the human brain and deals with computing systems known as artificial neural networks.
Be it machine learning or deep learning, the overall growth and popularity of the global AI market cannot be overlooked. From healthcare and finances to the virtual assistant on your smartphone, AI is everywhere.
Don’t believe us? Here are some stats to bolster our claim:
In the first quarter of 2001, a total of $28.5 billion was allocated to machine learning globally.
With a CAGR of 39.2 percent, the global deep learning market is projected to reach a valuation of $44.3 billion by 2027.
The deep learning software market in the USA is predicted to touch the $80 million mark by 2025.
The AI hardware market is predicted to reach a value of $87.68 billion with a CAGR of 37.6 percent during the forecast period 2019-2026.
No doubt, there can be no better time to take a deep learning certification and enter this field of blooming prospects. Here are some more stats to help you appreciate the global valuation of AI technologies.
6 Deep Learning Project Ideas For Beginners
A deep learning course will teach you the foundations of deep learning, how to build neural networks, and how to apply the knowledge while executing machine learning or deep learning projects. So, if you’re starting out in the field or are set to take a deep learning training, here are some project ideas that can be useful!
1.Classification of Numerical/Text Dataset: One of the simplest projects you can begin with is by using an artificial neural network to create a model that can classify numerical or textual data, such as classifying flower species based on measurement patterns.
2.Image Classification: Thi is another simple deep learning project idea for predicting the label of an input image. For instance, if you use a dataset of 3000 images containing three classes (say, lion, elephant, and dog) and use them as input, a convolutional neural network model will be able to classify them (that is, whether an image is of a lion, an elephant, or a dog) according to the highest probability.
3.Handwritten Digit Recognition: It is a popular project idea for beginners where the aim is to sort the given image of a handwritten digit into one of ten classes that represent integer values from 0 to 9. One of the best datasets for this project is the MNIST dataset which consists of 60,000 small square 28×28 pixel image collection (in grayscale) of handwritten single digits between 0 and 9. An alternate dataset is the Fashion-MNIST, where, instead of the numbers, the dataset comprises different fashion products divided into ten classes.
4.Captioning Images: As evident from the name, this fun and exciting project is all about predicting the caption for an input image. The best way to go about it is to use a convoluted neural network to extract features from the image and then using a decoder to generate a caption.
5.Text Summarization: The objective of this project is to generate meaningful summaries from news articles or paragraphs. A prominent real-world example of this is the Inshorts app. Since the project involves text generation, there is a need for a decoder and an encoder to encode information from a given paragraph. LSTM-based encoder-decoder works for small input sequence lengths. Otherwise, the Transformer models or attention mechanism can be used.
6.Image Segmentation: Have you ever wondered how self-driving cars navigate without randomly bumping into people or other inanimate objects? Well, it’s all because of a concept called image segmentation, where each object in an image is given a pixel-wise mask. In other words, a digital image is partitioned into multiple segments or sets of pixels on the basis of some characteristics. For example, all the buildings in an image will have the same pixel value that will be different from all the vehicles in the image, and so on. The most general approach for this project includes the creation of an encoder-decoder convolution neural network.
Conclusion
The field of artificial intelligence has come a long way and continues to evolve. While traditional machine learning has always been the talk of the town, deep learning has gone a notch higher to solve complex problems with large datasets, such as speech recognition, image classification, and natural language processing. So, whether it’s Facebook identifying faces correctly or Alexa answering your verbal questions, AI is everywhere. So, if you want to explore this exciting field of computer science, sign up for a deep learning course today and test your knowledge with these fun projects for beginners!
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