Deep Learning Training by Experts
Our Training Process

Deep Learning - Syllabus, Fees & Duration
MODULE 1
- Introduction to Tensor Flow
- Computational Graph
- Key highlights
- Creating a Graph
- Regression example
- Gradient Descent
- TensorBoard
- Modularity
- Sharing Variables
- Keras Perceptrons
- What is a Perceptron?
- XOR Gate
MODULE 2
- Activation Functions
- Sigmoid
- ReLU
- Hyperbolic Fns, Softmax Artificial Neural Networks
- Introduction
- Perceptron Training Rule
- Gradient Descent Rule
MODULE 3
- Gradient Descent and Backpropagation
- Gradient Descent
- Stochastic Gradient Descent
- Backpropagation
- Some problems in ANN Optimization and Regularization
- Overfitting and Capacity
- Cross-Validation
- Feature Selection
- Regularization
- Hyperparameters
MODULE 4
- Introduction to Convolutional Neural Networks
- Introduction to CNNs
- Kernel filter
- Principles behind CNNs
- Multiple Filters
- CNN applications Introduction to Recurrent Neural Networks
- Introduction to RNNs
- Unfolded RNNs
- Seq2Seq RNNs
- LSTM
- RNN applications
MODULE 5
- Deep learning applications
- Image Processing
- Natural Language Processing
- Speech Recognition
- Video Analytics
This syllabus is not final and can be customized as per needs/updates
 
			
													
												 
							

 
								 Participants in the deep learning course should have a thorough understanding of the principles of programming, as well as a solid understanding of the fundamentals of statistics and mathematics, as well as a clear grip on the critical knowledge portions of machine learning.  This deep learning course in Riffa is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning.  Deep learning is important because it automates feature generation, works well with unstructured data, has improved self-learning capabilities, supports parallel and distributed algorithms, is cost-effective, has advanced analytics, and is scalable. 
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network.  Students receive practical experience by working on real-world projects.  Deep learning is a type of learning that entails Specialization in Riffa will assist you in learning the fundamental ideas of deep learning, as well as understanding the problems, repercussions, and capacities of deep learning, as well as allowing you to contribute to the advancement of cutting-edge technology.  Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets.  Python is the language of deep learning.  Every day, businesses collect massive volumes of data and analyze it to get actionable business insights.  Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data.
							
			
Participants in the deep learning course should have a thorough understanding of the principles of programming, as well as a solid understanding of the fundamentals of statistics and mathematics, as well as a clear grip on the critical knowledge portions of machine learning.  This deep learning course in Riffa is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning.  Deep learning is important because it automates feature generation, works well with unstructured data, has improved self-learning capabilities, supports parallel and distributed algorithms, is cost-effective, has advanced analytics, and is scalable. 
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network.  Students receive practical experience by working on real-world projects.  Deep learning is a type of learning that entails Specialization in Riffa will assist you in learning the fundamental ideas of deep learning, as well as understanding the problems, repercussions, and capacities of deep learning, as well as allowing you to contribute to the advancement of cutting-edge technology.  Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets.  Python is the language of deep learning.  Every day, businesses collect massive volumes of data and analyze it to get actionable business insights.  Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. 									
									 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
									
									
									
								 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
												 
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                         
                                                    
                                               
                                        