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 Quebec
- Mobile App Develpment Courses in Quebec
- Website Design Courses in Quebec
- Networking and Security Courses in Quebec
- Digital Marketing (SEO) Courses in Quebec
- Graphics Training Courses in Quebec
- Cloud Computing Courses in Quebec
- Mean Stack Development Courses in Quebec
- Python Full Packages Courses in Quebec
- Animation & Video Editing Courses in Quebec
- Mern Stack Development Courses in Quebec
Course Highlights
Featured Courses
Artificial neural network systems are created on the human brain in deep learning, a subcategory of Machine Learning. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights.
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 Quebec is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning. Deep learning teaches using botorganizeded anorganizedtured data. Students receive practical experience by working on real-world projects.
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network.
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.
Because there is a strong demand for skilled deep learning engineers in various fields, this deep learning course in Quebec certification training is ideal for intermediate and advanced experts. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own.
