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
Python is the language of 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. Deep learning algorithms are employed in a variety of industries, from automated driving to medical gadgets.
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.
. Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own. Deep learning teaches using botorganizeded anorganizedtured data.
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network.
