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
- 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 jobsNEAR.in
- Training/Internship Certificate
- Web Development Courses in Indianpolis
- Mobile App Develpment Courses in Indianpolis
- Website Design Courses in Indianpolis
- Networking and Security Courses in Indianpolis
- Digital Marketing (SEO) Courses in Indianpolis
- Graphics Training Courses in Indianpolis
- Cloud Computing Courses in Indianpolis
- Mean Stack Development Courses in Indianpolis
- Python Full Packages Courses in Indianpolis
- Animation & Video Editing Courses in Indianpolis
- Mern Stack Development Courses in Indianpolis
Course Highlights
Featured Courses
Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. 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. Companies like to hire people who have completed this deep learning course. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. Python is the language of deep learning. Deep learning teaches using botorganizeded anorganizedtured data. Students receive practical experience by working on real-world projects. Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning.
.
