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Tag: learn Deep Learning

What Are Some Tips And Tricks For Training Deep Neural Networks?

Posted on by Imarticus Learning

Deep Neural Networks aid AI applications such as image and voice recognition to function at unprecedented accuracy. A Deep Neural network is basically an array of several layers, where each layer sieves raw data into a structured mathematical model. 

The process of making the data flow through the various layers is called Deep Neural Network Training. In humans, we also start recognizing an object once we have seen it several times. If you saw just one “car” in your entire life, you might not be able to recognize a car again if you saw a different model this time. 

In Data Science, this is easier said than done. Therefore, we have some tips and tricks that you can use when you sit down to teach your DNN to distinguish cars from trucks.

Normalization is Effective

Normalization layers help group logical data points into a higher consolidated structure. An apparent increase in performance has been recorded when using Normalization.

You can use it three ways;

  • Instance Normalization – If you’re training the DNN with small batch sizes. 
  • Batch Normalization – If you have a large batch size, supposedly more than 10, a batch normalization layer helps. 
  • Group Normalization – Independent of batch size, it divides the computation into groups to increase accuracy. 

Zero Centering 

Zero Centering is considered as an important process for preparing your data for training. Just like normalization, it helps in providing accurate results later. 

In order to zero center your data, you should move the mean of the data to 0. You can do this by subtracting the non-zeroed mean of the data from all the data inputs. This way, the origin of the data set on a scalar plane will lie on 0, making it Zero Centered.

Choose the Training Model Wisely 

One thing that you’ll come across when you learn Deep Learning, is that the choice of model can have a significant impact on training.   

Commonly, there are pre-trained models and there are models you train from scratch. Finalizing the right one that corresponds to your needs is crucial. 

Today, most DNN developers are using pre-trained models for their projects as they are resourceful in terms of the time and effort required to train a model. It’s also called Transfer Learning. VGG net and ResNet are common examples.  

The key here is the concurrency of your project with the pre-trained model. In case you can’t get a satisfactory model design, you can train a model from scratch too. 

Deal with Overfitting
Overfitting is one of the most popular problems in DNN training. It occurs when the live run of the training model yields exceptionally good results but the same wasn’t observed during the test runs. 

The problem is basically caused when the DNN starts accepting the attenuations as the perfect fit. This can be dealt with, using the technique of Regularization, which adjusts the problem of overfitting using an objective function. 

Conclusion

Wish you’d know more? Take up a deep neural network training course on Imarticus and start your progress today. DNNs are becoming increasingly popular in data science-related careers. Just like everything else, you can use the first-mover advantage with pro-active learning. 

How Can You Learn Deep Learning Quickly?

Posted on by Imarticus Learning

 

Why is Deep Learning important to learn in today’s world of ever-changing technologies? Human capabilities to do tasks especially on very large volumes of data are limited. AI stepped in to help train computers and other devices to aid our tasks. And how does it do so? The evolved devices use ML to learn by themselves recognizing data patterns and arriving at predictions and forecasts very much like the human brain. Hence one would need to learn all of the above-mentioned concepts to even reach the deep-learning possibility.

In order to learn ML, one would need to have knowledge of Java, R or Python and suites like DL4J, Keras, and TensorFlow among others depending on the areas you are interested in. It is also important to have the Machine Learning Course before one delves into deep-learning. And yes there is a lot of statistics, probability theory, mathematics and algebra involved which you will have to revise and learn to apply.

 

If you are interested in learning Deep Learning quickly, here are the top four ways to do so.

A. Do a course: One of the best ways is to scour the net for the best top free MOOC courses or do a completely paid but skill oriented course. Many are online courses and there are classroom courses as well. For the working professional course from a reputed training partner like Imarticus Learning makes perfect sense. Just remember that to learn Deep learning you will need to have access to the best industry-relevant solutions and resources like mentoring, assured placements, certification and of course practical learning.

B. Use Deep Learning videos: This is a good resource for those with some knowledge of machine learning and can help tweak your performance. Some of the best resources of such videos are ML for Neural Networks by the Toronto University, the tutorials of Stanford University on Deep Learning, ConvNet resources on Github, and videos by Virginia Tech, E and CE, the Youtube, etc.

C. Community Learning: There are communities available online like the Deep Learning community and r-learning communities from Quora, Reddit, etc. Such communities can be of immense help once you have a firm grasp of the subject and need to resolve or are practicing your skills.

D. DIY books: There is a wealth of books available to learn Deep Learning and understand the subject better. Do some research on the best deep-learning resources, the limits of it, differences between ML and deep-learning, and such topics. DIY books are easy to read and hard to practice with. Some excellent books are the TensorFlow-Deep Learning, Nielsen’s Neural Networks-and-Deep Learning, and Chollet’s Python and Deep Learning.

The Disadvantages:

  1. Rote knowledge is never really helpful and the syllabus is very vast and full of complicated subjects.
  2. The practice is the key is only acquired through constantly doing relevant tasks on relevant and industry-standard technology.
  3. Mentorship is very important to learn the current best practices.
  4. Time is a constraint, especially for working professionals.
  5. The best value courses are often paid-for courses.
  6. DIY is bereft of certification and hence a measure of your skills.
  7. The DIY approach may also never train you for the certification exams.
  8. Assured placements in the paid for courses are a huge draw for freshers making a career in deep-learning.
  9. There are non-transferable soft-skills that you require and do not find in the packages.
  10. Industry acceptance is often sadly lacking for the self-learning candidates.

Conclusion:

Learning is always a process where reinforcement and practice scores. Though there are many options available to do deep-learning for free and on one’s own, the route is never easy. Thus it seems the paid courses, like the one at Imarticus Learning, is definitely a better bet. Especially if the course is combined with mentorship of certified trainers, assured placements, widely accepted certification, personalized personality-development modules and a skill-oriented approach with tons of practice as the one at Imarticus is.

The Imarticus Learning courses deliver well-rounded and skilled personnel and offer a variety of latest technology courses which are based on industry demand.

Given the above information, the quickest way to master deep-learning definitely appears to be doing a course at Imarticus. If you want to be job-ready from day one, then don’t wait. Hurry and enroll. We have multiple centers in India – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi, Gurgaon and Ahmedabad. So you can consider as per your need!!