Deep Learning & Neural Networks

In this comprehensive AI specialization, you will undergo structured modules focused on real-world applications, industry-relevant tools, and hands-on experience.

Online Instructor-led Interactive Sessions:

• Course 1: Foundations of Deep Learning & Perceptron Models

• Course 2: Python for Deep Learning & Data Preprocessing

• Course 3: Neural Networks (ANN) — Forward & Backpropagation

• Course 4: Convolutional Neural Networks (CNN) for Image Processing

• Course 5: Recurrent Neural Networks (RNN), LSTM & GRU

• Course 6: Transfer Learning & Pretrained Models

• Course 7: Natural Language Processing with Deep Learning

• Course 8: Deep Learning with PyTorch and TensorFlow

• Course 9: Model Optimization, Regularization & Hyperparameter Tuning

• Course 10: Model Deployment on Cloud with Flask & Streamlit

• Capstone: Full-stack Deep Learning Project + Model Deployment

This deep learning course will equip you with practical and theoretical skills to build AI solutions:

• Artificial Neural Networks (ANN): Understand how machines simulate the human brain

• Convolutional Neural Networks (CNN): Build deep learning models for image classification

• Recurrent Neural Networks (RNN): Work with sequential data like time series or language

• Transfer Learning: Reuse pre-trained networks like VGG, ResNet, BERT for faster development

• NLP with Deep Learning: Build sentiment analysis, chatbot, translation & text generation models

• TensorFlow & PyTorch: Learn industry-leading deep learning frameworks

• Model Deployment: Use Flask, Streamlit, and FastAPI to serve models in real-world apps

• Hyperparameter Tuning: Optimize model performance through experimentation

• GPU Computing: Speed up model training using CUDA-enabled environments

Deep Learning is at the heart of modern AI systems — from self-driving cars to ChatGPT.
As industries adopt AI to automate and innovate, professionals skilled in neural networks are in high demand.
Career paths include AI Engineer, Computer Vision Specialist, NLP Engineer, and ML Researcher — with competitive salaries and global opportunities.

• Design and implement deep learning models for various domains

• Collect and preprocess large datasets for training

• Optimize model architectures and tune hyperparameters

• Train and evaluate models using GPU hardware

• Interpret and visualize neural network behavior

• Deploy models into production for web/mobile/cloud applications

• Stay updated with the latest AI research and frameworks

With this course, you’ll be job-ready within 6–7 months, with a strong foundation in AI.