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Artificial Intelligence_Swayam Recordings
Module 1 - Introduction to Artificial Intelligence & Introduction to Python
Intrductio to AI (56:07)
Anaconda Installation and Overview (16:06)
Python Basics (78:31)
Numpy (44:58)
Pandas (40:35)
Module 2 - Deep Learning With Tensorflow
Introduction to ANN (39:49)
Steps to ANN (11:17)
Regression Problem (30:48)
Binary Problem (37:17)
Multiclass Problem (35:05)
Introduction to CNN (6:08)
Types of Images (6:34)
CNN Architecture Overview (4:03)
Convolution layer (9:29)
Pooling layer (3:06)
Flattening layer (2:01)
Fully Connected layer (3:06)
CNN Pratical (50:54)
Introduction to RNN (3:09)
Deep Dive into RNN (4:36)
Key Components (7:31)
Types of RNN (3:27)
Implementation Notebook for RNN Types (23:18)
Many-Many RNN type (8:43)
Architecture Explanation (18:07)
RNN upto model building (25:50)
RNN prediction (7:02)
Vanishing Gradient (6:01)
RNN Key Takeaways (2:11)
LSTM Intro (5:47)
LSTM Deep Dive (4:17)
Key Components LSTM (9:53)
LSTM Architecture (12:16)
LSTM scaling (8:28)
LSTM Model Building Prediction (24:26)
LSTM plot (11:11)
LSTM takeaways (9:21)
Module 3 - Transfer Learning
Introduction to TL-01 (4:53)
Workflow and Applications of TL-02 (4:06)
Inception V1 - 3 (4:44)
Inception V2 and V3- 04 (13:36)
Inception V3 Coding - 05 (19:34)
VGG-16 and VGG-19 Theory- 06 (18:48)
VGG-16 and VGG-19 Model Exploring Coding- 07 (17:44)
VGG-16 and VGG-19 Coding on Customised dataset- 08 (22:52)
ResNet - 50 Theory- 9 (23:50)
ResNet50- Model Exploration Coding - 10 (7:04)
ResNet50- Coding on customized dataset - 11 (8:24)
Module 4 - Time Series
Introduction to Time Series - 01 (1) (8:14)
Components of Time Series - 02 (1) (12:57)
Key Models - 03 (1) (11:27)
Auto Regression Coding - 04 (1) (25:46)
ARIMA and SARIMA Coding-05 (1) (19:06)
Module 5 - Flask
Flask Introduction (1) (6:23)
Other Frameworks (1) (2:10)
Flask Framework (7:14)
Why Flask (3:06)
Building Webapp using Flask (9:16)
Flask Practical (32:38)
CNN Deployment (78:47)
VGG-16 and VGG-19 Theory- 06
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