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The Future of Deep Learning: Predictions and Opportunities
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07th Sep, 2023
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Have you ever come across those apps and ads on technology sites where you can upload pictures of Albert Einstein, and these pictures miraculously come to life as if they were right there? Some apps even have the ability to transform a young face into an elderly one within seconds, astonishingly accurate in capturing facial features with a whopping 99% precision. If you've ever had a blast experimenting with different poses and reactions on Snapchat, then you've already experienced a taste of this technological magic.
Little did you know, this magic and enchantment is powered by the fascinating world of Deep Learning. In simpler terms, Deep Learning is a concept within the world of Machine Learning that mimics human intelligence and trains computers to think and learn like us. In this blog, we're going to dive into the captivating world of Deep Learning, exploring its incredible potential, the exciting future it holds, and the lucrative career options available in this rapidly expanding field. Buckle up and get ready to be amazed!
What is Deep Learning?
In formal terms, Deep Learning is a part of Machine Learning (ML), where artificial networks (neural) imitate large units of data. ANN or Artificial Neural Network is a complex algorithm that works like humans.
Like humans use their brains to use their senses and reactions. It’s the chemical and electrical exchange of neurons that occur in the brain, because of which we react and act. Similarly, artificial neural nets (ANN) have several interconnected nodes (neurons), through which they can act like humans. Deep learning, based on predictive modeling, works on ANNs that have more than 3 layers of neural nets, while many have more than 100. Let us take a look at the deep learning past present and future analysis.
The Evolving Role of a Deep Learning Engineer
A Deep Learning Engineer’s job role is to create learning models and algorithms, which can help computers consume massive amounts of data, and then make predictions based on that. Deep learning and the future of AI revolve around know-how and expertise in Data Science, Machine Learning, Software Engineering, and Algorithm Coding.
With Time, the role of a deep learning engineer has evolved, and currently, it is more about Data preparation, Algorithm Design, Deployment, and Training and Evaluation. We have already come across some of the hybrid job titles like Deep Learning Software engineers, deep learning researchers and scientists, deep cleaners and more. The top industries where deep Learning has become an indispensable part are Technology Companies, Finance and Healthcare Companies, and Transportation Companies. To work on various modes and intelligence factors, a deep learning engineer should excel in Programming, mathematics, data handling, problem-solving, and neural networks. You must enroll yourself in a full-time Data Science course, to know more about these concepts.
Are Deep Learning Engineers in Demand?
Deep learning engineers are the future of technology and AI. This concept will always bring something new and shall not fade out. It has been forecasted that by 2024, deep learning career requirements will accelerate by 50%, which is two times the demand for other IT jobs. It has also been estimated that by 2027 the Deep Learning market will exceed USD 21 billion.
Approximately, the average salary of a deep learning engineer in the US is $159,981. The least he can earn is $92,913 and he can touch the maximum limit of $275,460. In comparison, the salary of similar job holders is as follows:
| Machine Learning Engineer | Average $155,381 per year |
| Data Scientist | Average $127,438 per year |
| Research Scientist | Average $91,838 per year |
| Data Engineer | Average $133,054 per year |
| Data Analyst | Average $74,598 per year |
| Software Engineer | Average $114,435 per year |
Considering the above salary chart, it is evident how graciously deep-learning engineers get paid, mainly because of the skyrocketing demand for professionals in various fields, industries, and organizations.
Will Deep Learning Be a Good Career?
The average salary of a deep learning engineer varies between $92,913 and $275,460. It is a great and high-paying career, which requires you to narrow down your focus to one of the subparts of the wide space of deep learning. You can undergo specialized Deep Learning Certification training, can get close to a salary like that.
With this a career, you'll be able to be the driving force of the industry that will rule humanity in coming future.
The Deep Learning Future: Where is Deep Learning Going?
The deep learning future is slowly becoming a Giant, and the area needs experts and professionals to work on. If we become ignorant of the process, there is a chance that the deep learning future technology will grow into something so complex, which may not be handled easily. Thus, the growing demand for deep learning professionals! AI industry leaders have proposed to democratize Machine Learning and make DL a primary part of ML, where the experts can work on supervised automation and can control risks that persist. The deep learning future is going far and wide, and moving too fast, so there is a dire need for professionals who understand the concept and run it; even control it if required!
Capsule Networks
Also called CapsNets, it is a new deep neural network architecture that was introduced in 2017 by Geoffrey Hinton and his team. Capsule networks work with vectors and make calculations on the inputs which move as the orientation of the image is changed. This use of CNNs for object recognition is unlike human perception which should be further built to resolve issues like rotation and scaling and enhance the deep learning architecture.
Automatic Feature Extraction
This works on a critical step called feature extraction to allow algorithms to transform and represent data in a simpler form and to perform a task like classifying data into a few categories. Domain experts are required to function this technique, which no longer requires manual feature extraction. This uses neural networks to figure out raw data automatically without human intervention. It consists of several processing layers of neural nets to enhance the feature complexity and represent the input data in the best possible way to give the required output.
Transfer Learning - Introduction of non-learning-based AI approaches to Deep Learning
Transfer learning is an innovative concept that is about creating and comprehending a neural network that is specialized to work on one task. You can also use it again for any new but related task. The initial and middle layers of a pre-trained model can detect generic features and transfer them to another network. Then you can train the later layers to recognize more specific features. Transfer learning uses public and third-party models to build the use case.
The non-learning-based AI approaches to Deep Learning are:
- Unsupervised learning where reasoning and problem-solving are conducted at a more abstract level
- Symbol-manipulation and hybrid models integrate deep learning with symbolic systems, excelling at inference and abstraction
- Insights into cognitive and developmental psychology to gain common sense, understand the innate machinery in humans minds and understand narrative
- Multi-dimensional generalized artificial and natural intelligence to deal with the complexity of the world
Few-shot learning (FLS)
Few-shot learning (FSL) is a subfield of machine learning which works on a small amount of training data. This technique handles data shortage and computational costs and is critical in healthcare to detect rare diseases. Such models can strengthen deep learning models with new research and developments.
Use of DL in Neuroscience
It has been researched how deep learning can understand theories of the brain. ANN models of the brain analyze the neurobiological data and possess multiple layers that resemble brain regions. Deep learning involves training hierarchical ANNs in an end-to-end manner. With deep learning networks, these models can be trained to perform various tasks and collaborate well with biological accuracy.
General Adversarial Networks (GAN)
Generative adversarial networks (GANs) work closely with data augmentation applications. The result is to create meaningful new data by using unlabelled original data. They work with deep learning models using GANs-based data augmentation to generate synthetic data/training data. This helps them reduce data collection needs and perform better in comparison to the traditional augmentation method
Use of Edge Intelligence
Edge computing is all about providing analysis, storage, and control to work on scalability and latency after tackling diverse IoT settings. When combined with AI, edge computing extends the technologies from robust computing models to ML-based smart practices to achieve edge computing in IoT settings. It is still a nascent stage for researching edge intelligence for IoT using machine learning and has already been applied to several aspects like healthcare, education, management, engineering, and the economy.
NLP at the Next Level
Natural Language processing goes on to the next level comprising Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Encoder-decoder sequence-to-sequence:
- CNN helps to see a document as an image and uses a matrix of words instead of pixels.
- RNN uses deep learning process words through n-grams or a window (CNNs).
- Autoencoders are deep learning encoder-decoders that approximate a mapping from X to X, i.e., input=output.
- Encoder-decoder sequence-to-sequence is an adaptation to autoencoders specialized for translation, summarization, and similar tasks.
- Transformers are a model that relies entirely on a self-attention mechanism to draw global dependencies between input and output.
The revolution in NLP in recent years applies to various tasks including 3D images, and using models like Jurassic-X, BLOOM, and Turing-NLG.
Adoption of a Core Set of Standard Tools
You should be thorough with the core set of de facto tooling frameworks, most of which are open source. Some of the most popular tools include Caffe, Torch, BigDL, TensorFlow, Theano, OpenDeep, and MXNet.
Deep Learning Engineer Career Trajectory and Deep Learning Future Roles
New Deep learning demands deep learning engineers be well-equipped with neural programming and supporting abilities. You must acquire skills based on Adam, Dropout, RNNs, Convolutional networks, softmax, RELU, linear regression, LSTM, Batch Norm, and Xavier/He initialization.
The deep learning future scope includes:
1. Software engineers with AI, Neural advancement, and Data sciences. The need is to perform work in particular application spaces with an extraordinary skill of a neural analysis
2. Data Engineer – You should be able to work on computational strategies and Natural sciences and must have a general competency in biomedical sciences.
3. Instructors – This includes Ph.D.-level Data Engineers
4. Data Science designers work on new calculations and neural network analysis for building and conveying Neural computational apparatuses. Some of the top deep learning future applications and roles you can consider as a profession while learning deep learning are:
- Software Engineer
- Research Analyst
- Data Analyst
- Data Scientist
- Data Engineer
- Neuroinformatics
- Bioinformatician
- Image Recognition
- Software Developer
- Research Scientist
- Research Fellow
- Instructor for Deep Learning
- Applied Scientist
- Full Stack Web Developer for Deep Learning
- Lead Manager – Deep Learning
- Natural Language Process Engineer
How to Start Your Deep Learning Career?
To have a future scope of deep learning, you first have to excel as a data scientist or an ML engineer. You need to be thorough with concepts about Mathematics, probability, programming, statistics, and supervised and unsupervised learning approaches. You must take an online course and get expertise on various future applications of deep learning like ML/DL libraries and frameworks, Python-based languages, and work on various ML models.
To know more about deep learning neural networks and the future of AI, you must create new AI-powered systems. You should be skilled in Software Engineering, Data Skills, Frontend/UI Technology, Cloud Technology, and soft skills. Follow these steps to start a successful career in deep learning:
Step 1 – Get your bachelor’s degree in computer science or a similar field.
Step 2 – Get some experience working as an ML professional or data scientist/data engineer
Step 3 – Get higher education, certifications, and online course training. KnowledgeHut’s full time Data Science course will help you derive the best know-how and hands-on skills in deep learning. https://www.knowledgehut.com/data-science-courses
Step 4 – Improve your Mathematical abilities, Programming abilities, Data Engineering Knowledge, Machine Learning Understanding, Understanding of Deep Learning Algorithms, and Understanding of Deep Learning Frameworks.
Conclusion
In conclusion, the future of deep learning, often confused with Artificial Intelligence and Machine Learning, holds immense promise as a subset of ML. Specifically built upon artificial neural networks, its power lies in its ability to analyze and forecast using unstructured datasets, revolutionizing the operations of numerous industries, even in its nascent stage.
So, as we gaze into the horizon of the deep learning landscape, we can anticipate a future brimming with opportunities. From healthcare diagnostics to autonomous vehicles, from personalized recommendations to enhanced customer experiences, the applications of deep learning are vast and varied.
By embracing this field, staying updated with the latest developments, and honing your skills, you can position yourself at the forefront of this transformative industry. So, embrace the immense possibilities that lie ahead, and embark on an exciting career journey in the dynamic world of deep learning.
Frequently Asked Questions (FAQs)
1. Does deep learning have a future?
Deep learning is a concept that is required indispensably in situations that require predictions, and accuracy, and involve large amounts of data. It is about creating models that mimic human behavior, staying within the norms of research and consumer data protection. The deep learning future is bright for deep learning engineers.
2. What is next after deep learning?
After the rise of deep learning, the technology shifts to a third layer which can be supervised learning, unsupervised learning, or reinforcement learning. The way ahead depends on various techniques of machine learning.
3. What is the salary in deep learning?
The salary of a deep learning engineer can vary approximately from $92000 to $ 254000 per year
4. Is deep learning a hard course?
If you are already through with the basics of technical learning, then Deep Learning is not a complicated process. You simply need to be well-equipped with programming languages, NLP, Image and Speech recognition, and predictive modeling to start your deep learning course.
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