Section 1: Course Overview

✅ DAY–1 Introduction to Deep Learning
✅ DAY–2 Basic Computer Vision

Section 2: Artificial Neural Network

✅ DAY–3 Neurons & Perceptron
✅ DAY–4 Activation Function
✅ DAY–5 Gradient Descent

✅ DAY – 6 Stochastic Gradient Descent
✅ DAY – 7 Backpropagation
✅ DAY – 8 Artificial Neural Network – Project 1

Section 3: Deep Neural Network

✅ DAY – 9 Optimization Algorithms – SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
✅ DAY – 10 Batch Normalization
✅ DAY– 11 Hyperparameter tuning
✅ DAY– 12 Interpretability
✅ DAY– 13 Deep Neural Network – Project 2

Section 4: Convolutional Neural Network

✅ DAY– 14 Convolutional Neural Network & its Layers
✅ DAY– 15 CNN Architecture
✅ Day–16 Different frameworks on Deep Learning (Tensorflow, Keras, PyTorch & Caffe)
✅ Day-17 Object Recognition using Pre Trained Model – Caffe – Project 3
✅ Day-18 Image classification using Convolutional Neural Network from Scratch – Tensorflow & Keras – Project 4
✅ Day-19 Custom Image Classification using Transfer Learning – Project 5
✅ Day-20 YOLO Object recognition – Project 6
✅ Day 21 Image Segmentation – Project 7
✅ Day 22 Project using MxNet – Project 8
✅ Day 23 Project using PyTorch – Project 9
✅ Day 24 Social Distancing detector – Project 10
✅ Day 25 Face Mask detector – Project 11

Section 5: Recurrent Neural Network

✅ Day 26 Introduction to RNN and LSTM
✅ Day 27 Project using RNN – Project 12

Section 6:

✅ Day 28 Introduction CUDA Toolkit and cuDNN for deep learning
✅ Day 29 Getting started with the Intel Movidius Neural Compute Stick – Project 13
✅ Day 30 Custom Object classification using Nvidia Jetson – Project 15