Deep Learning (DL) Course
There is no such thing as the best Course or the worst ones. Sounds strange but this is true! No matter what a training institute claims about its training faculty or infrastructure, you might find that most of the times all the claims were hollow.
Due to the huge demand of Deep Learning (DL), training institutes offering Deep Learning (DL) Course are mushrooming even in dingy lanes across the country. So, you need to check thoroughly over the web about the authenticity of all the claims. What matters is that there should be a good student-teacher ratio, infrastructure, and provision of imparting hands-on training so that the learning outcomes are positive.
Deep learning is a branch of machine learning which is completely based on artificial neural networks, as neural network is going to mimic the human brain so deep learning is also a kind of mimic of human brain. In deep learning, we don’t need to explicitly program everything. The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture.
Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.
What jobs will Deep Learning course prepare you for? This program is designed to build on your skills in deep learning. As such, it doesn’t prepare you for a specific job, but expands your skills in the deep learning domain. These skills can be applied to various applications and also qualify you to pursue further studies in the field.
Enrolling for Deep learning course is important because learning is very important course these days as every student and professional want to learn this course. It’s very important you learn from experts or at least 5–8 years of relevant experience in ML AI Deep learning.
Why should one enrol to learn Deep Learning Course? In this program, you’ll master deep learning fundamentals that will prepare you to launch or advance a career, and additionally pursue further advanced studies in the field of artificial intelligence. You will study cutting-edge topics such as neural, convolutional, recurrent neural, and generative adversarial networks, as well as sentiment analysis model deployment. You will build projects in Keras and NumPy, in addition to TensorFlow PyTorch.
Pre-requisite knowledge you need to have while learning Deep Learning Course? This program has been created specifically for students who are interested in machine learning, AI, and/or deep learning, and who have a working knowledge of Python programming, including NumPy and pandas. Outside of that Python expectation and some familiarity with calculus and linear algebra, it’s a beginner-friendly program.
Deep Learning is a specialised subset of Machine Learning. A few examples of Deep learning are driverless cars, voice-controlled devices like MG cars, and home-assistance devices like Alexa etc. Deep learning applications are used to identify images captured by satellites. Today this technology is used in detecting new planets to fight Corona and cancers. This technology teaches computers and machines to mimic human behaviour and learn from them accurately. A successful deep learning application might need huge volume of labelled data and humungous computing power but with new research, we can apply techniques like transfer learning and get state of the art result even with less data. This is one of the main reasons of popularity for deep learning.
Some of the famous deep learning models are:
Elman Networks
LSTMs
GANs
YOLO
Seq2Seq with attention
BERT
CNNs
Etc.
Deep Learning Syllabus
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LESSON 1
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Introduction to deep learning
Neural Networks Basics
Shallow neural networks
Improving Deep Neural Networks
Neural Networks and Deep Learning
Deep Neural Networks
LESSON 2
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Practical aspects of Deep Learning
Optimization algorithms
Improving Deep Neural Networks
Hyper-parameter tuning, Batch Normalization and Programming Frameworks
LESSON 3
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Natural Language Processing & Word Embedding
Sequence models & Attention mechanism
CNNs : Convolutional Neural Networks Sequence Models
Recurrent Neural networks
LESSON 4
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Foundations of Convolutional Neural Networks
Special applications: Face recognition & Neural style transfer
Deep convolutional models: case studies
Object detection
What comes under Deep Learning Course? Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.
Happy Learning
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