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Internship on Deep Learning (30 Days)

Day-1 | Introduction to Data Science & Studies

Concept of Data Science (52:42)
Attachments

Day - 2 | Computer Vision

Basics of Computer Vision (49:31)

Day - 3

Neurons and Perceptron (52:54)

Day - 4

Activation Function (32:09)

Day-5

Gradient Descent (32:39)

Day - 6

Gradient Descent Types (26:16)

Day -7

Backpropogation (62:20)

Day - 8

Diabetes Detection using NN (54:21)

Day - 9

Optimizers (48:47)

Day-10

Batch Normalization (43:21)

Day- 11

Hyperparameter Tuning (32:39)

Day 12

InterPretability (59:36)

Day 13

Deep Neural Network (44:34)

Day 14 Convolutional Neural Network

Convolutional Neural Network and Its Layer (48:32)

Day- 15

CNN Architecture (52:58)

DAY 16

Different frameworks on Deep Learning (Tensorflow, Keras, PyTorch & Caffe) (45:51)

DAY 17

Object Recognition using Pre Trained Model – Caffe (42:33)

DAY 18

Image classification using Convolutional Neural Network from Scratch – Tensorflow & Keras (42:32)

DAY 19

Custom Image Classification using Transfer Learning (43:39)

Day 20

YOLO Object recognition (39:21)

DAY 21

Image Segmentation (29:41)

DAY 22

Object Recognition Using Mxnet (28:24)

DAY 23

Object Recognition using Pytorch (33:03)

DAY 24

Social Distance Monitoring (37:30)

DAY 25

Face Mask detector (49:25)

DAY 26

Introduction to RNN and LSTM (30:39)

DAY 27

Project using RNN LSTM (45:26)

DAY 28

Introduction CUDA Toolkit and cuDNN for deep learning (43:31)

DAY 29

Getting started with the Intel Movidius Neural Compute Stick (25:06)

DAY 30

Nvidia (49:45)

Mandatory Step First Step - Join the Community Here

Mandatory Step First Step - Join the Community Here

Top 100 Interview Question

Deep Learning

Teach online with

Nvidia

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