Home Courses Applied Machine Learning Online Course Attention Models in Deep Learning

Attention Models in Deep Learning

Instructor: Applied AI Course Duration: 84 mins

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LIVE: Encoder-Decoder Models
Transformers and BERT
1.1
History of Neural networks and Deep Learning.
25 min
1.2
How Biological Neurons work?
8 min
1.3
Growth of biological neural networks
17 min
1.4
Diagrammatic representation: Logistic Regression and Perceptron
17 min
1.5
Multi-Layered Perceptron (MLP).
23 min
1.6
Notation
18 min
1.7
Training a single-neuron model.
28 min
1.8
Training an MLP: Chain Rule
40 min
1.9
Training an MLP:Memoization
14 min
1.10
Backpropagation.
26 min
1.11
Activation functions
17 min
1.12
Vanishing Gradient problem.
23 min
1.13
Bias-Variance tradeoff.
10 min
1.14
Decision surfaces: Playground
15 min
1.15
Interview Questions
30 min
2.1
Deep Multi-layer perceptrons:1980s to 2010s
16 min
2.2
Dropout layers & Regularization.
21 min
2.3
Rectified Linear Units (ReLU).
28 min
2.4
Weight initialization.
24 min
2.5
Batch Normalization.
21 min
2.6
Optimizers:Hill-descent analogy in 2D
19 min
2.7
Optimizers:Hill descent in 3D and contours.
13 min
2.8
SGD Recap
18 min
2.9
Batch SGD with momentum.
25 min
2.10
Nesterov Accelerated Gradient (NAG)
8 min
2.11
Optimizers:AdaGrad
15 min
2.12
Optimizers : Adadelta andRMSProp
10 min
2.13
Adam
11 min
2.14
Which algorithm to choose when?
5 min
2.15
Gradient Checking and clipping
10 min
2.16
Softmax and Cross-entropy for multi-class classification.
25 min
2.17
How to train a Deep MLP?
8 min
2.18
Auto Encoders.
27 min
2.19
Word2Vec :CBOW
19 min
2.20
Word2Vec: Skip-gram
14 min
2.21
Word2Vec :Algorithmic Optimizations.
12 min
3.1
Tensorflow and Keras overview
23 min
3.2
GPU vs CPU for Deep Learning.
23 min
3.3
Google Colaboratory.
5 min
3.4
Install TensorFlow
6 min
3.5
Online documentation and tutorials
6 min
3.6
Softmax Classifier on MNIST dataset.
32 min
3.7
MLP: Initialization
11 min
3.8
Model 1: Sigmoid activation
22 min
3.9
Model 2: ReLU activation.
6 min
3.10
Model 3: Batch Normalization.
8 min
3.11
Model 4 : Dropout.
5 min
3.12
MNIST classification in Keras.
18 min
3.13
Hyperparameter tuning in Keras.
11 min
4.1
Biological inspiration: Visual Cortex
18 min
4.2
Convolution:Edge Detection on images.
28 min
4.3
Convolution:Padding and strides
19 min
4.4
Convolution over RGB images.
11 min
4.5
Convolutional layer.
23 min
4.6
Max-pooling.
12 min
4.7
CNN Training: Optimization
9 min
4.8
Receptive Fields and Effective Receptive Fields
8 min
4.9
Example CNN: LeNet [1998]
10 min
4.10
ImageNet dataset.
6 min
4.11
Data Augmentation.
8 min
4.12
Convolution Layers in Keras
17 min
4.13
AlexNet
13 min
4.14
VGGNet
11 min
4.15
Residual Network.
22 min
4.16
Inception Network.
19 min
4.17
What is Transfer learning.
23 min
4.18
Code example: Cats vs Dogs.
15 min
4.19
Code Example: MNIST dataset.
6 min
4.20
[Interview Question] How to build a face recognition system?
1 min
5.1
Why RNNs?
23 min
5.2
Recurrent Neural Network.
29 min
5.3
Training RNNs: Backprop.
16 min
5.4
Types of RNNs.
14 min
5.5
Need for LSTM/GRU.
10 min
5.6
LSTM.
35 min
5.7
GRUs.
7 min
5.8
Deep RNN.
7 min
5.9
Bidirectional RNN.
12 min
5.10
Code example : IMDB Sentiment classification
33 min
6.1
Live session on Generative Adversarial Networks (GAN)
124 min
7.1
LIVE: Encoder-Decoder Models
82 min
9.1
Transformers and BERT
112 min
10.1
Live session on Image Segmentation
95 min
11.1
Object Detection
123 min
11.2
Object Detection YOLO V3
103 min
12.1
GPT-1, 2 and GPT-3 Models
130 min
13.1
Code Walkthrough (OpenCV using Python)

13.2
Design and build a Smart Store
14.1
Questions and Answers
30 min
15.1
Code Walkthrough: Tensorflow 2.0 + Keras
15.2
Code Walkthrough: Tensorflow 2.0 + Keras --II
15.3
Code Walkthrough: DC-GANs and Gradient Tape
15.4
Code Walkthrough: Time Series forecasting using LSTMs/GRUs
15.5
Code Walkthrough Live Session: BERT and Fine-Tuning
15.6
Code Walkthrough: BERT- QuestionAnswering System
15.7
Code-Walkthrough: Transformers from scratch-I
15.8
Code-Walkthrough: Transformers from scratch-II
15.9
LIVE: Problem solving [Classification Algorithms]
15.10
Vision Transformers [from ICLR 2021]
15.11
Interview Questions on Deep Learning (NN, MLP, CNNs)
15.12
Interactive Interview Questions(from top product based companies)

15.13
Scenario based interview questions in AI/ML/DataScience

15.14
LIVE : Explainable AI [ LIME & SHAP ]
15.15
LIVE_ Explainable AI [ SHAP ]
15.16
Cutting-edge Recommender system for travel-booking by NVIDIA Engineers
15.17
Case-based Live: Deep Learning based Recommender Systems [ Algos + Design ]
15.18
Entity Embeddings @ LinkedIn, Amazon Session-1
15.19
Entity Embeddings @ LinkedIn, Amazon Session-2
15.20
Graph Neural Nets & Geometric Learning - PART 1
15.21
Graph Neural Nets & Geometric Learning --Part-2
112 min
15.22
Live Session: Graph Neural Nets & Geometric Learning --Part-3
86 min
15.23
Graph Neural Nets & Geometric Learning --Part-4
84 min
15.24
Interactive Interview Session on NLP (Classical to SOTA)
15.25
Live Session(02nd July 2022): Interactive Interview Session on DL (MLPs)
1 min
15.26
Live Session(31st July 2022): Interview Session on CNNs
1 min
15.27
Live Session(28th Aug 2022): Knowledge Distillation in Deep Learning
1 min
15.28
Live Session(30th Oct 2022): Scenario based interview Questions
1 min
15.29
Live Session(04th Dec 2022): ML System Design of Image Search Engine
1 min
15.30
Live Session(22nd January 2023): Interview Session for ML Engineer roles