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 Mumbai
- Mobile App Develpment Courses in Mumbai
- Website Design Courses in Mumbai
- Networking and Security Courses in Mumbai
- Digital Marketing (SEO) Courses in Mumbai
- Graphics Training Courses in Mumbai
- Cloud Computing Courses in Mumbai
- Mean Stack Development Courses in Mumbai
- Python Full Packages Courses in Mumbai
- Animation & Video Editing Courses in Mumbai
- Mern Stack Development Courses in Mumbai
Course Highlights
Featured Courses
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. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. Students receive practical experience by working on real-world projects. 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.
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. Companies like to hire people who have completed this deep learning course.
. Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data.
Rather than being numerical, the majority of the data is in an unstructured format, such as audio, image, text, and video.
