NTPL-(NOVRIS TECHNOLOGIES PRIVATE LIMITED.)

About Novris Technologies

Novris Technologies (NTPL Training & Development Private Limited) is an education platform since 2022 providing rigorous industry-relevant programs designed and delivered in collaboration with world- class faculty and industry for Deep learning.

Course Description

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data.

Requirements

GPU (Graphics Processing Unit)
Memory
Storage
Deep Learning Framework
Python
Datasets
Data Preprocessing
Understanding of Deep Learning Concepts
Programming and Python Skills
Model Selection and Evaluation
Training and Optimization
Data Visualization

Deep learning algorithms are designed to automatically learn and improve from experience by analyzing large amounts of labeled or unlabeled data. The learning process involves training a neural network on a dataset and adjusting the network's weights and biases to minimize the difference between predicted and actual outputs. This is typically achieved using optimization techniques like gradient descent and backpropagation.

One of the key advantages of deep learning is its ability to learn hierarchical representations of data. Deep neural networks can automatically learn multiple levels of abstraction, with each layer of the network learning increasingly complex features or concepts.

Module 1: Introduction

Overview
- Introduction to Deep learning
- What is Data Structures
- Deep Learning Framework
- Historical development and key milestones in deep learning
Installation
- Set up your development environment
- Install Python packages and dependencies
- Install Python packages and dependencies
- PyTorch
- MXNet
- Install GPU support
- Additional libraries and tools

Module 2: Architecture, Scenarios, and Admin Tools Deep learning

Architecture & scenarios
- Convolutional Neural Networks (CNNs)
- Convolutional Neural Networks (CNNs)
- Transformers
- Autoencoders
- Image Classification
- Object Detection
- Model Training and Tuning
- . Model Interpretability and Explainability

Module 3: Deep Learning Basics

Variables
- Deep Learning Basics
- Activation functions
- Backpropagation algorithm
- Gradient descent optimization

Module 4: Recurrent Neural Networks (RNNs) Deep learning

Recurrent Neural Networks (RNNs)
Long Short-Term Memory (LSTM) networks
Applications in natural language processing and speech recognition

Module 5: Training Deep Neural Networks Deep learning

- Regularization techniques (dropout, batch normalization)
- Optimization algorithms (Adam, RMSprop)
- Hyperparameter tuning
- Transfer learning and fine-tuning

Module 6: Advanced Deep Learning Topics Deep learning

- Generative models (variational autoencoders, generative adversarial networks)
- Deep reinforcement learning
- Deep reinforcement learning
- Explainability and interpretability in deep learning

Module 7: Hands-on Projects

Implementing deep learning models using TensorFlow or PyTorch
Image classification project
Natural language processing project
Optional: Custom project based on student's interest

Module 8: : Evaluation and Assessment

Quizzes and assignments
Practical coding projects
Final exam (theory and implementation)

Module 9: :Prerequisites

Basic understanding of linear algebra and calculus
Familiarity with programming concepts (Python recommended)
Some knowledge of machine learning concepts (e.g., supervised learning, optimization)

Module 10: Placement Guide

Tips to clear an Interview
Common Interview questions and answers
Deep Learning Interview Questions and Answers
Resume Building Guide
Career roadmap and certifications
Attempt for Deep Learning Certification Exam
Start applying for Jobs

Owen Christ

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Material Includes

About Novris Technologies

Course Description

Requirements

Module 1: Introduction

Module 2: Architecture, Scenarios, and Admin Tools Deep learning

Module 3: Deep Learning Basics

Module 4: Recurrent Neural Networks (RNNs) Deep learning

Module 5: Training Deep Neural Networks Deep learning

Module 6: Advanced Deep Learning Topics Deep learning

Module 7: Hands-on Projects

Module 8: : Evaluation and Assessment

Module 9: :Prerequisites

Module 10: Placement Guide

Owen Christ

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Devit Killer

January 29, 2021

Owen Christ

January 29, 2021

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