Deep learning is an area of Machine Learning Deep learning is a part of machine learning that teaches the computers to learn by examples. Deep learning is the prime technology which is behind the driverless cars, virtual assistants and other artificially intelligent machines. Deep learning is getting more and more popular among the masses and is providing results which were not possible to achieve before. In deep learning technology, a model is trained from a huge amount of input data. The model is trained through training input data. This data can be in the form of text, images or any type of input data. Deep Learning is becoming useful in the current scenerio because:
Deep learning needs a huge amount of labeled data. For example, image recognition requires a large amount of input data. Same is the case with the driverless cars.
Deep learning requires a huge amount of computing power. High performance learning is required for the efficient working of deep learning.
Deep learning applications are used in industry applications like automation, medical equipments, self driving etc. Deep learning is applicable in aerospace and defence. Deep learning is used to identify the objects from satellites and identify the areas of interest. Deep learning is also used to identify the cancer cells and help in cancer research. Deep learning also helps in industrial enviornment to improve the safety and working conditions of the workers. It also helps in automating the repititive tasks and processes.
Deep learning is one of the trending technology. This technology is being used by technological giants like Google, Amazon, Tesla etc. To become proficient in deep learning, you have to learn the concepts of deep learning. If you want to undergo 6 months industrial training in Chandigarh in Deep Learning, contact DummyByte. DummyByte is the best industrial training institute in Chandigarh offering courses in AI, machine learning, deep learning, Android, Java etc.
Date and Time: Tick, time tuple, current time, getting formatted time, getting calender.
Python Function: Defining a function, calling a function, overloading concept, function arguments, required arguments, keyword arguments, default arguments, variable length arguments, anonymous function, return statements, concept of variables.
History of Neural Networks, Introduction to Deep Learning Theory, Introduction to Deep Learning and Neural Networks.
NumPY/SciKit Learn basics.
Introduction to Tensorflow and Theano.
Introduction to Keras, demonstration of Neural Network, building a basic Neural Network.
Neural network internals, activation functions, backpropagation, loss functions, weight inilialization.
Data normalization/Standardization, model tuning, deployment, and scaling, Deep Network topologies, feed Forward
How to Choose an Appropriate Neural Network
Tuning, overfitting, learning rate, adaptive learning rates, dropout, regularization
Advanced topics, import Keras into deeplearning4j for production, model Import,transfer learning, model serializer.