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 Punjab
- Mobile App Develpment Courses in Punjab
- Website Design Courses in Punjab
- Networking and Security Courses in Punjab
- Digital Marketing (SEO) Courses in Punjab
- Graphics Training Courses in Punjab
- Cloud Computing Courses in Punjab
- Mean Stack Development Courses in Punjab
- Python Full Packages Courses in Punjab
- Animation & Video Editing Courses in Punjab
- Mern Stack Development Courses in Punjab
Course Highlights
Featured Courses
Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. 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 models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. Deep learning is a type of learning that entails Specialization in Punjab 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. This deep learning course in Punjab is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning. Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets.
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. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own.
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.
