This Deep Learning course introduces key concepts and applications in the field of machine learning, focusing on neural networks and advanced techniques such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The course covers practical implementation of these models in various real-world applications, from image classification to natural language processing and time series predictions. Through hands-on projects, learners will gain practical experience using deep learning tools and frameworks.

Overview:  (9 hours 55 minutes)

The course provides an in-depth understanding of deep learning techniques and their implementation using Python-based tools. It explores both the theoretical foundation and practical applications of various deep learning models, offering learners opportunities to work on real-world projects. By the end of the course, students will have developed the skills to build and deploy deep learning models for various tasks, including computer vision, object detection, speech recognition, and more.

Projects:

1. Age and Gender Detection with Deep Learning for Real-Time Applications: Develop a model that can detect a person’s age and gender based on images using deep learning.
2. Bitcoin Price Prediction: Implement a deep learning model to predict cryptocurrency prices, focusing on time series forecasting.
3. Automatic Parking Vehicle Verification and Parking System using Number Plate Recognition: Build a system that automatically verifies vehicles and manages parking using number plate recognition.
4. Person Counting System using Deep Learning: Design a system to count people in an area using deep learning techniques for surveillance and crowd management.
5. Vehicle Speed Estimation: Develop a model to estimate the speed of vehicles from images or video feeds.
6. Face Recognition: Implement a face recognition system using deep learning for identity verification and access control.
7. Fire Detection using Flame Detector in Deep Learning: Build a deep learning model to detect fire and flame hazards using image data.
8. Social Distance Detection: Create a model to detect social distancing in public spaces using deep learning.
9. Handwritten Recognition using Deep Features: Implement a model for recognizing handwritten characters using convolutional neural networks.
10. Driver Drowsiness and Yawn Detection: Build a model to monitor and detect signs of driver drowsiness or yawning to prevent accidents.
11. Covid-19 Chest X-ray Image Detection using Deep Learning: Develop a model to detect COVID-19 from chest X-ray images using deep learning.
12. Sign Language Based Hand Gesture Recognition: Create a system that recognizes hand gestures in sign language using deep learning.
13. Optical Character Recognition (OCR) using OpenCV: Implement an OCR system that can read and convert printed text into machine-readable text.

Tools Required:

1. Python programming language, particularly libraries such as TensorFlow, Keras, and PyTorch for implementing deep learning models.
2. OpenCV for computer vision tasks like object detection and image processing.
3. Jupyter Notebooks for interactive coding and model development.
4. TensorFlow and Keras for building neural networks, including CNNs and RNNs.
5. Scikit-learn for data preprocessing and basic machine learning tasks.
6. Matplotlib and Seaborn for visualizing data and model performance.
7. Google Colab or other cloud platforms for running computations and hosting models.
8. Pre-trained models such as VGG16, ResNet, and YOLO for fine-tuning in specialized tasks.

Skills Required:

1. Familiarity with Python programming and basic machine learning concepts.
2. Understanding of neural networks, including their architecture, training, and evaluation.
3. Knowledge of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep learning concepts.
4. Ability to preprocess and handle large image datasets for computer vision tasks.
5. Experience in using machine learning libraries like TensorFlow, Keras, and PyTorch.
6. Problem-solving skills to apply deep learning models to real-world applications.
7. Understanding of data augmentation techniques and transfer learning for better model performance.
8. Knowledge of object detection, image classification, and regression techniques in deep learning.

Day-wise Topics List:

Day 1 - Introduction to Deep Learning Concepts

Day 2 - Neural Networks and its Classification

Day 3 - Artificial Neural Networks (ANN) & Application

Day 4 - Convolutional Neural Network (CNN) with Application

Day 5 - Recurrent Neural Network and its Applications

Day 6 - Age and Gender Detection with Deep Learning for Real-Time Applications

Day 7 - Bitcoin Price Prediction

Day 8 - TensorFlow

Day 9 - Keras

Day 10 - Automatic Parking Vehicle Verification and Parking System using Number Plate Recognition

Day 11 - Person Counting System using Deep Learning

Day 12 - Vehicle Speed Estimation

Day 13 - Face Recognition

Day 14 - Automatic Multi-Class Classification of Food Ingredients using Deep Learning

Day 15 - Detection Algorithms in Deep Learning

Day 16 - Fire Detection using Flame Detector in Deep Learning

Day 17 - Image to Text, Text to Speech Conversion

Day 18 - Social Distance Detection

Day 19 - Handwritten Recognition using Deep Features

Day 20 - Head Pose Estimation in the Wild Using CNN and Adaptive Gradient Methods

Day 21 - Driver Drowsiness and Yawn Detection

Day 22 - Drone Detection using OpenCV

Day 23 - Face Emotion Detection

Day 24 - Automatic Leaf Characteristics Detection using Neural Networks

Day 25 - Face Recognition using OpenCV and Deep Learning

Day 26 - YOLO Object Detection using OpenCV Technique

Day 27 - Face Clustering using Deep Learning

Day 28 - Covid-19 Chest X-ray Images Detection using Deep Learning

Day 29 - Smart Glasses and Sign Language Based Hand Gesture Recognition

Day 30 - Optical Character Recognition using OpenCV