Deep Learning Online Live Class by Experts
Our Training/Internship 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
- Since 2001, Making IT Experts by Experts
- Duration: 40 Hours (Vary as per your skill)
- Course Fees
- Individual Live Classes (1:1)
- Batch Classes @ Low Fees
- Training on Your Time, Any Where
- Access to Recorded Videos
- Practical Internship on Projects
- 100% Placement Support by our jobsHybrid.com
- Training/Internship Certificate
- Agra
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- Bangalore
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- Coimbatore
- Delhi
- Dindigul
- Erode
- Gulbarga
- Guntur
- Gurgaon
- Haryana
- Hyderabad
- Idukki
- Indore
- Jaipur
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- Kannur
- Kanpur
- Kasaragod
- Kerala
- Kolkata
- Kollam
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- Mangaluru
- Mumbai
- Mysuru
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- Navi Mumbai
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- Pune
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- Salem
- Shimla
- Solapur
- Surat
- Thane
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- Thrissur
- Tiruchirappalli
- Tiruppur
- Trivandrum
- Vadodara
- Varanasi
- Vellore
- Vijayawada
- Visakhapatnam
- Wayanad
Course Highlights
Our Locations
Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning. Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. Students receive practical experience by working on real-world projects. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. This deep learning course in Idukki 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.
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. Python is the language of deep learning.
The foundations of deep learning and neural networks are covered, as well as techniques for improving neural networks, strategies for organizing and completing machine learning projects, convolutional neural networks, and their applications, recurrent neural networks and their methods and applications, and advanced topics such as deep reinforcement learning, generative adversarial networks, and adversarial attacks. 
