Deep Learning

What is Deep Learning? Everything You Need to Know

What is Deep Learning?

Deep Learning (DL) is the new hot cake in AI world and can be described as a subset of Machine Learning. Deep learning uses special type of Artificial Neural Network architectures i.e. FCN, CNNs RNNs, and Attention etc to understand the data. The ultimate aim of these neural networks is to teach machines to do complex tasks without the need for programming. In simple words, deep learning is the higher level of automation compared to traditional machine learning models, which tries to teach machines to understand complex human behaviours like language and vision. Let’s take the example of driverless cars. These cars are driven by ML techniques. However, DL algorithms like object detection and YOLO, enable the car to distinguish a lamppost from a person standing on the pavement. Without deep learning autonomous cars were not safe. So, you can see how important and relevant DL is to building futuristic technology.

Deep Learning Vs Machine Learning Vs AI

Let’s take a top-down approach to explain artificial intelligence, machine learning, and deep learning.

AI vs ML vs DL

Artificial Intelligence (AI): Artificial Intelligence can be described as a broad concept that tries to replicate human intelligence. It includes a host of technologies, from the Good Old Fashioned AI to the latest Deep Learning.

Machine Learning (ML): As the name says, this technology helps machines to self-learn. Rather than giving hand written rules, in traditional ML models we pass lots of data as input and the model tries to learn its own rules for the specific task. ML is a subset of artificial intelligence.

Deep Learning (DL): Deep Learning is a subset of Machine Learning technique where we take automation to another level. In traditional ML models we need to give the data and set of handpicked features for the model to learn properly, where as the DL selects its own set of features, by reducing human intervention. Ex, CNNs use convolutional layers and filters to understand the shapes and edges inside an image, we don’t need to mention what are the set of structures of an apple to detect it as an apple.

AI vs ML vs DL

How Deep Learning Works?

Deep Learning works based on a neural network architecture that features an input layer, hidden layers, and an output layer. More is the number of Hidden layers, more complex information we can learn for the data. The neural network can have up to 150 hidden layers, justifying the name deep neural networks. The points of connection between layers are called nodes. Artificial Neural Networks have many verities, for example Fully Connected Networks (FCNs), CNNs (convolutional Neural Network), GANs (Generative Adversial Networks), RNNs (Recurrent Neural Networks), LSTM (Long Short Term Memories) etc. Mostly deep learning is used for two tasks, i.e. Image processing and Natural Language Processing. CNNs and GANs are mostly used for image modelling and RNNs, LSTMs etc are used for NLP. Recently, we have advanced with new models such as transformers for NLP tasks.

Deep Learning Methods

Deep learning is a complex task and optimizing the deep learning model is still a challenging for researchers. Here, we are throwing some of the most common names that you might come across while learning.

Vanishing and Exploding Gradients

To optimize the deep learning models we use optimizers such as SGD, ADAM, RMSProp etc. While doing the training sometimes we will observe that the model is not updating with number of epochs and loss is not decreasing. This happens due to vanishing gradients. When we have lots of hidden layers in the networks for CNNs or it is a simple vanilla RNN, vanishing gradient is a common case. To deal with vanishing gradients we use skip connections or LSTMs. But in some cases the Eigen value of the weight matrix is more than 1, resulting the exploding gradients, which can again solved by gradient clipping.

Transfer Learning

Transfer learning is one of the core reasons why deep learning is getting more popular. Transfer learning helps us to fight the constraint that we need lots of data to train a deep learning model. Image classification tasks can apply transfer learning models with pre training on ImageNet dataset and recently in NLP, BERT can also use this method to train NLU models for smaller text data. Transfer learning is a process where you take a pre-trained model and modify its certain layers to fit to task at hand, thus you don’t need to optimize all the parameters.

Dropout Layers

Overfitting is a challenge to any deep learning model and to deal with overfitting traditional machine learning models use regularization. Though regularization can also be applied to deep neural networks such as L1 and L2 Norms, the common technique we use to deal overfitting in deep learning is called as dropout. In this method we drop certain nodes of certain layers randomly from the network during training with a dropout probability. By doing so, the model will not able to remember the data patterns and thus can deal with the memory problem. Be careful not to use the dropout layer while validation. Deep learning libraries like Keras and PyTorch can help us to implement these techniques easily.

Why is Deep Learning Important?

Deep Learning is niche and powerful. Data is the new oil and with deep learning we can analyse the data faster than ever. Due to the automatic feature extraction process, deep learning can save us lot of time, and with the high end GPUs at hand, training time has reduced significantly. Our day to day work is already influenced by deep learning and all the major companies and industry have started using and investing in AI. Automation is the future of humanity. Data enabled companies will grow at a speed of 27% to add additional revenue of 1.8 trillion USD to the economy by the end of 2021, which is not possible without AI and deep learning.

Examples of Deep Learning at Work  

Automated Driving

Automated Driving DL Example
In automated driving, DL algorithms help the automobile differentiate between moving and stationary objects. It also allows the vehicle to differentiate a person from a post on the road. The neural network brings together all the data captured by the cameras and sensors fixed in the vehicle. It then analyses the data and sends a command to the steering wheel.

Aerospace and Defence

Aerospace and Defence DL Example
HPEC (High-Performance Embedded Computing), high-speed switched serial links, and rugged standardized form factors can be used in military signal intelligence (SIGINT) and synthetic aperture radar (SAR) applications. Using Data Science techniques and methodologies, the defence can collate, sort, and classify large volumes of data. The data can be analysed to gain insights and streamline decision making. As DL techniques get more advanced, they can be used for better results in the aerospace and defence sectors. DARPA and Boston dynamics are the great example of how AI is changing the defence.

Medical Research

Medical Research DL Example

DL methodologies are used in the healthcare sector for

  • Disease recognition and identification
  • Delivering personalised treatment
  • Drug discovery and manufacturing
  • Clinical trial research
  • Radiotherapy
  • Smart electronic records
  • Early stage cancer detection
  • Predict epidemic outbreaks

Industrial Automation

Industrial Automation DL Example
Deep Learning algorithms can be used for predictive maintenance and repairs. They can also be used to automate and optimize the supply chain. These methodologies can also be used in reprogramming the existing workflow the increase production and quality. Siemens, GE, and KUKA are few of the companies that have already begun implementing DL-based automated solutions in their manufacturing process.

Why Learn Deep Learning?

Gartner hype cycle has proved that AI is no more an option, it is a must to have skill in 2020. As we saw in the previous sections, Deep Learning methodologies and techniques are being used in many sectors. Also, many businesses are now starting to explore this emerging technology to automate processes, improve workflow, and enhance quality. This has in turn increased the demand for skilled DL professionals. Improve your career prospects by taking up a Deep Learning course in Bangalore or any other major city.

In the course, make sure to learn all the DL tricks and techniques, which will help you in the workplace. Also, you gain certification from a reputed institute, which will improve your chances of getting placed in a top organisation.

What are the Job Opportunities in Deep Learning?

There are numerous job opportunities available for skilled and certified professionals in this domain. A few of the job profiles are:

  • DL engineer
  • ML engineer
  • Data Scientist
  • Data analyst
  • NLP engineer
  • Computer Vision experts
  • Research scientist
  • Neuroinformatician
  • Bioinformatician
  • Software developer
  • Applied scientist
  • Deep Learning Instructor/Trainer
  • Full stack web developer for Deep Learning applications

What is the Salary Package of Deep Learning?

The average salary is around INR 8 lakhs per annum. Freshers can expect a salary package of around INR 5 to 8 lakhs per annum. Professionals in NLP and Vision, with 2 years of experience can expect an earning up to INR 16 lakhs per annum. The actual salary will depend on various factors such as certification, experience, city, and company.

Who Can Join Deep Learning Course?

The prerequisites for joining Deep Learning training in Bangalore or elsewhere are knowledge of statistics, linear algebra, probability, calculus, and programming languages such as Python. It will also be useful if you have basic knowledge of data science and data analytics tools and techniques. Computer science or Math freshers who are interested in building a career in this emerging domain can fill the learning gaps and join this course.

Why Learn “Deep Learning” Courses Through Corpnce?

Corpnce is a corporate training house that offers Deep Learning courses in Bangalore and committed to deliver the highest standard of teaching. We follow a project-based learning methodology that will help students learn to apply the techniques in real world and students can opt to be part of our internship program where they can help the company to create deep learning products. Moreover, Corpnce has a team of highly trained faculty from industry and research fields. The curriculum of the course is planned under the guidance of the experts to ensure that the syllabus covers the latest developments in this field. We cover both the Vision and NLP in details for our students. Above all, we also offer placement assistance for deserving students. To reduce the financial burden for those who join our Deep Learning training in Bangalore, we offer EMI options by coordinating with our banking partners.

Join our intensive training program in Deep Learning and get started with world’s top technology. Register online now!