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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 (25:05)
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
Introduction to RNN and LSTM
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