Deep Learning Online Live Class by Experts
Our Training/Internship 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
Deep Learning Jobs in Seeb
Enjoy the demandFind jobs related to Deep Learning in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Seeb, chennai and europe countries. You can find many jobs for freshers related to the job positions in Seeb.
Deep Learning Internship/Course Details
This deep learning course in Seeb is mainly recommended for software engineers, data scientists, data analysts, and statisticians who are interested in deep 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. Students receive practical experience by working on real-world projects.
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.
Rather than being numerical, the majority of the data is in an unstructured format, such as audio, image, text, and video. Deep learning models in the real world could be used for driverless cars, money filtration, virtual assistants, facial recognition, and other applications. Companies like to hire people who have completed this deep learning course. Every day, businesses collect massive volumes of data and analyze it to get actionable business insights. Deep learning teaches using botorganizeded anorganizedtured data. Meet a Few of our Industry Experts 🚀 Your Pathway to IT Career
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