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 Lausanne
- Mobile App Develpment Courses in Lausanne
- Website Design Courses in Lausanne
- Networking and Security Courses in Lausanne
- Digital Marketing (SEO) Courses in Lausanne
- Graphics Training Courses in Lausanne
- Cloud Computing Courses in Lausanne
- Mean Stack Development Courses in Lausanne
- Python Full Packages Courses in Lausanne
- Animation & Video Editing Courses in Lausanne
- Mern Stack Development Courses in Lausanne
Course Highlights
Featured Courses
One of the key benefits of employing deep learning is its capacity to perform feature engineering on its own. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. This deep learning course in Lausanne is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep learning.
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. 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 teaches using botorganizeded anorganizedtured data.
Deep learning is a subset of machine learning (ML), which is essentially a three-layer neural network. Students receive practical experience by working on real-world projects. Deep learning has become increasingly significant for commercial decision-making since it is very adept at processing such forms of data. Deep learning powers a variety of AI (artificial intelligence) services and applications that automate and perform physical operations without the need for human participation.
