Certified Deep Learning Professional
Why should you take this Certification?
This certification will make you Internationally Certified and will help in growing your career.
This certification will help you to get Job & Freelance opportunities from thousands of companies.
Average salary given to a Certified Deep Learning Professional is around $60,000 per annum.
Exam Cost: USD 20.00 5 out of 5 based on 7647 ratings.become certified WhatsApp us share
What Is Deep Learning?
Deep learning is a subset of a larger family of machine learning techniques based on representation learning and artificial neural networks. There are three types of learning: supervised, semi-supervised, and unsupervised.
Deep-learning architectures like deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been used in areas like computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs.
Information processing and dispersed communication nodes in biological systems inspired artificial neural networks. ANNs differ from biological brains in a number of ways. In particular, neural networks are static and symbolic, whereas most live animals' organic brains are dynamic (plastic) and analogue.
In deep learning, the word "deep" refers to the employment of numerous layers in the network. A linear perceptron cannot be a universal classifier, but a network with a nonpolynomial activation function and one hidden layer of unlimited width can, according to early research. Deep learning is a more recent form that has an unbounded number of layers of bounded size, allowing for practical application and optimization while maintaining theoretical universality under moderate conditions. For the sake of efficiency, trainability, and understandability, deep learning layers are also allowed to be heterogeneous and stray substantially from biologically informed connectionist models, hence the "structured" portion.
Salary Range of A Deep Learning Professional
Depending on the experience level and the demographic area, the salary of a Deep Learning Professional varies widely.
The following is the average Deep Learning Professional Salary in USA:
|Best Minds In Deep Learning||$100,000|
|Senior Deep Learning Professionals||$ 85,000|
|Intermediate Deep Learning Professionals||$ 65,000|
|Deep Learning Freshers||$ 50,000|
The following is the average Deep Learning Professional Salary in India:
|Best Minds In Deep Learning||INR 120,000|
|Senior Deep Learning Professionals||INR 90,000|
|Intermediate Deep Learning Professionals||INR 70,000|
|Deep Learning Freshers||INR 50,000|
What Is Deep Learning Certification?
Deep Learning Certification assesses a person's knowledge of deep learning as well as their understanding of digital concepts. A variety of certifying authorities, ranging from government agencies to commercial enterprises and organisations, offer the Deep Learning certification. Certifications are normally obtained by the completion of an online or offline exam.
All certificates have their own set of benefits, such as international recognition, career opportunities, freelancing, and so on. So, Deep Learning certification is an online exam that evaluates a Professional's skills and knowledge in order to match them with the suitable opportunities.
Why should you take this Online Deep Learning Certification?
The online Deep Learning certification from Loopskill will assist you in becoming a certified Professional. You can take this exam and by scoring 70% you will become an internationally certified Deep Learning Professional. This certification will help you in three different ways:
- You can demonstrate your Deep Learning certification to potential employers and can stand out of the crowd.
- You can apply for great jobs using loopskill website or app; moreover, our partners companies will contact you directly for full-time or part-time opportunities depending on your skills & requirements.
- Loopskill is not just a platform to get certified or to find full time jobs; here being a certified Professional you can also do freelancing for the clients around the globe. You will be approached by the clients who need your help in building some web based platform or some app based platform.
The loopskill’s online Deep Learning certification is created to help people in exploring and achieving their full potential so they can get connected to the best opportunities around the globe.
Deep Learning Application
Deep Learning is the primary factor behind autonomous driving. A million sets of data are loaded into a system to create a model, train the machines to learn, and then evaluate the outcomes in a secure setting. The Uber Artificial Intelligence Labs in Pittsburg are focusing on not only making driverless cars more common, but also integrating smart features such as food delivery possibilities with the use of autonomous vehicles.
News aggregation and the detection of fake news
There is now a technique to remove all of the negative and offensive items from your news stream. Deep learning is being used extensively in news aggregation, which is assisting efforts to personalise news for individual readers.
Processing of Natural Language (NLP)
Understanding the complexity of language, such as syntax, semantics, tonal nuances, expressions, and even sarcasm, is one of the most difficult jobs for humans to master. Humans acquire suitable answers and a personalized form of expression to every event as a result of constant training since birth and exposure to various social settings. Deep Learning-based Natural Language Processing aims to achieve the same goal by teaching machines to recognize linguistic nuances and formulate suitable answers.
Virtual Personal Assistants
Virtual assistants, such as Alexa, Siri, and Google Assistant, are the most widespread use of deep learning. Each engagement with these assistants allows them to gain a better understanding of your voice and accent, giving you a second human connection experience. Deep learning is used by virtual assistants to learn more about their subjects, which might range from your dining preferences to your most visited places or favorite songs.
A source of entertainment (VEVO, Netflix, Film Making, Sports Highlights, etc.)
To auto-generate highlights for telecast, Wimbledon 2018 employed IBM Watson to analyze player moods and expressions through hundreds of hours of footage. They saved a lot of time and money as a result of this. They were able to include in crowd response and match or player popularity thanks to Deep Learning, resulting in a more accurate model.
Recognized by sight
Consider browsing through a collection of old photographs that takes you down memory lane. You decide to frame a handful of them, but first you need to sort through them. In the lack of metadata, the only way to accomplish this was to put in manual work. The most you could do was arrange them by date, but often downloaded photographs don't have that metadata. With Deep Learning, photos may now be classified based on locations recognized in photographs, faces, a group of people, or events, dates, and so on.
Detection of Fraud
The banking and finance sector, which is afflicted with the task of fraud detection as money transactions grow digital, is another domain that benefits from Deep Learning. Autoencoders in Keras and Tensorflow are being created to detect credit card fraud, which will save financial institutions billions of dollars in recovery and insurance costs. Fraud prevention and detection are based on spotting patterns in consumer transactions and credit scores, as well as spotting anomalies and outliers. For fraud detection, machine learning techniques such as classification and regression, as well as neural networks, are used.
“From medical imaging to genome analysis to drug discovery, the whole healthcare business is undergoing transformation, and GPU computing is at the heart,” according to NVIDIA. GPU-accelerated apps and systems are bringing new efficiencies and possibilities to physicians, clinicians, and researchers who are dedicated to improving the lives of others.
Every platform is now attempting to deploy chatbots to give personalized experiences with a human touch to its visitors. Deep Learning is assisting e-commerce behemoths such as Amazon, E-Bay, Alibaba, and others in their efforts to give seamless tailored experiences in the form of product recommendations, personalized packages and discounts, and spotting huge revenue potential during the holiday season. Even in newer markets, reconnaissance is carried out by presenting items, offerings, or plans that are more likely to appeal to the human psyche and contribute to micromarket expansion.
Sound Effects for Silent Films
Synthesizing sounds to complement silent videos is a use of both convolutional neural networks and LSTM recurrent neural networks. To identify acceptable sounds for the scene, a deep learning model prefers to correlate video frames with a database of pre-recorded sounds. This work is accomplished by the use of training 1000 films, which feature drum sticks striking various surfaces and producing various noises.
Handwriting Generation by Machine
This Deep Learning application entails the creation of a new set of handwritings for a given corpus of words or phrases. When the samples were developed, the handwriting was effectively presented as a sequence of coordinates employed by a pen. The connection between the movement of the pen and the letters is discovered, and new examples are created.
Important Topics to Learn & Master in Deep Learning
Introduction to AI and Deep Learning
- History of AI: SL, UL, and RL
- Object Detection
- Applications of Deep Learning
- Challenges for Deep Learning
- Deep Learning Full Cycle
- Perception of Shallow Neural Network
- Training a Perceptron
- Activation Functions
- Dropout layer
Introduction Deep Neural Network
- Designing a Deep Neural Network
- Loss Function
- Tools for Deep Learning Models
- Tensorflow – Ecosystem
- TFlearn Pytorch
- SGD, Momentum, NAG,
- Adagrad, Adadelta, RMSprop, Adam
- Batch Normalization
- Exploding and Vanishing Gradients
- Hyperparameter Tuning
- Width vs Depth
Success and History CNN Network
- Design and Architecture
- Deep Convolutional Models
- Sequence Data
- Sense of Time
- RNN Introduction
Future of Deep Learning
The following are some of the major trends that will shape the future of deep learning:
- The current rise of deep learning research and industrial applications demonstrates its "ubiquitous" presence in every area of AI, from natural language processing to computer vision.
- With enough time and effort, unsupervised learning methods could produce models that closely resemble human behavior.
- The seeming tension between consumer data protection rules and high-volume consumer data research demands will continue.
- The inability of deep learning technologies to "reason" is a barrier to automated decision-support aids.
- DeepMind Technologies' acquisition by Google holds potential for worldwide marketers.
- To stay useful, future machine learning and deep learning technologies must exhibit learning from limited training materials, transfer of learning across contexts, continuous learning, and adaptive capabilities.
- Deep learning may not be the sole AI solution savior, despite its global popularity.
- Developers may soon find themselves overtaken and compelled to undergo intense training if deep learning technology research continues at its current rate.
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