Veda IT

Who Should Join Deep Learning Course?

Job Switchers
work professionals
Engineering Graduates
University Students
Entry Level Candidates

Keyskills of Deep Learning Developer

A Certificate Program in Deep Learning focuses on skills such as proficiency in Python and knowledge of mathematical concepts like linear algebra, calculus, and probability. Core skills include expertise in neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Proficiency in frameworks like TensorFlow, PyTorch, or Keras is essential, along with experience in training, fine-tuning, and deploying deep learning models. Skills in handling large datasets, image and text processing, and optimizing model performance are crucial.

Key Features

Covers neural networks, CNNs, RNNs, transfer learning, and GANs.
Real-world projects and exercises for practical deep learning skills.
Learn from experienced deep learning practitioners and data scientists.
Offline classes with optional online support.
Recognized certificate from Veda IT upon successful course completion.

What you'll learn

The Certificate Program in Deep Learning at Veda IT is an advanced course designed to provide an in-depth understanding of deep learning principles, neural networks, and their applications across various domains. This program is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to gain expertise in building and deploying deep learning models. The curriculum covers essential topics including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transfer learning, and generative models, using popular tools such as TensorFlow, Keras, and PyTorch.

Through hands-on projects and labs, students will gain experience in designing, training, and optimizing deep neural networks for tasks in computer vision, natural language processing, and more. By the end of the course, graduates will be prepared for roles in AI and deep learning, or for further studies in advanced AI applications.

Modules Covered

Overview of Deep Learning and AI Applications
Basics of Neural Networks and Perceptrons
Forward and Backpropagation in Neural Networks
Setting Up TensorFlow and Keras for Model Building
Building and Training Feedforward Neural Networks
Model Evaluation Metrics (Accuracy, Loss, Confusion Matrix)
Mini Project: Building and Evaluating a Simple Neural Network

Understanding Image Processing and Convolutions
Building CNNs for Image Classification
Pooling, Padding, and Strided Convolutions
Architectures: VGG, ResNet, Inception
Transfer Learning with Pre-trained Models
Image Augmentation Techniques for Better Generalization
Mini Project: Image Classification with CNNs

Introduction to Sequential Data and RNNs
Understanding Long Short-Term Memory (LSTM) and GRU Networks
Sequence Modeling and Text Generation with RNNs
Introduction to Transformers and Attention Mechanisms
Working with Natural Language Processing (NLP) Tasks
Mini Project: Sentiment Analysis or Text Generation with LSTMs

Introduction to Generative Adversarial Networks (GANs)
Building and Training Simple GANs for Data Generation
Hyperparameter Tuning and Model Optimization
Model Deployment with Flask and Docker
Introduction to Cloud Deployment (AWS, GCP)
Best Practices for Production-Ready Deep Learning Models
Capstone Project: End-to-End Deep Learning Application with Deployment

Learning Path

Introduction to Neural Networks and Deep Learning:

Learn the fundamentals of neural networks, activation functions, backpropagation, and deep learning frameworks.

Convolutional Neural Networks (CNNs) for Computer Vision

Master CNN architectures and apply them to image recognition, object detection, and visual data analysis.

Recurrent Neural Networks (RNNs) and NLP Applications:

Explore RNNs, LSTMs, and GRUs to solve natural language processing (NLP) tasks such as text generation and sentiment analysis.

Advanced Deep Learning - GANs, Model Tuning, and Deployment:

Dive into advanced topics like Generative Adversarial Networks (GANs), hyperparameter tuning, and model deployment in production

Mini Projects for Hands-On Experience:

Apply your skills to small-scale projects to reinforce your knowledge in different deep learning applications.

Capstone Project and Career Guidance:

Build a comprehensive deep learning solution and receive expert guidance to advance your career in AI and deep learning.

Potential Roles

Deep Learning Engineer
AI Specialist
Data Scientist (Deep Learning-Focused)
Computer Vision Engineer
NLP Engineer
AI Research Assistant
Machine Learning Engineer (Advanced)

Start Date20/05/2025
Enrolled100
Lectures50
Skill LevelBasic
LanguageEnglish,Telugu
Quizzes10
CertificateYes
Pass Percentage100%

Certificate Program in Deep Learning

Upon successful completion of the Certificate Program in Deep Learning, you will receive a certificate from Veda IT, validating your skills in neural networks, computer vision, NLP, and deep learning deployment.

Enroll Now

Design and Developed by Colourmoon

Deep Learning

Who Should Join Deep Learning Course?

Job Switchers

work professionals

Engineering Graduates

University Students

Entry Level Candidates

Keyskills of Deep Learning Developer

Key Features

What you'll learn

Modules Covered

Module 1: Introduction to Neural Networks and Deep Learning 3 hours

Module 2: Convolutional Neural Networks (CNNs) for Computer Vision 3 hours

Module 3: Recurrent Neural Networks (RNNs) and NLP Applications 3 hours

Module 4: Advanced Deep Learning - GANs, Model Tuning, and Deployment 3 hours

Learning Path

Potential Roles

Certificate Program in Deep Learning