The author of the videos of this channel, Dr. Kshitiz Verma, LLB, is a B.Tech. in CSE from IIT Kanpur and also holds MS and Ph.D. in CS. He also holds a bachelor of law (LLB).

The author is currently a faculty member in the department of CSE at JK Lakshmipat University, Jaipur.

Lecture 37A: Revisiting the word2vec architecture
49:30
Lecture 37A: Revisiting the word2vec architecture
Lecture 48: Transformers -- Self Attention
41:21
Lecture 48: Transformers -- Self Attention
Lecture 49: Transformers continued..
44:52
Lecture 49: Transformers continued..
Lecture 47: Introduction to Transformers
49:36
Lecture 47: Introduction to Transformers
Lecture 46: Introduction to Attention Mechanism.
51:33
Lecture 46: Introduction to Attention Mechanism.
Lecture 45: LSTM -- Long Short Term Memory
46:04
Lecture 45: LSTM -- Long Short Term Memory
Lecture 44: Exploding and vanishing gradients in RNNs
45:39
Lecture 44: Exploding and vanishing gradients in RNNs
Lecture 43: Language Modeling using RNNs
48:02
Lecture 43: Language Modeling using RNNs
Lecture 42: Bi-directional RNNs and Introduction to Language Modeling
45:49
Lecture 42: Bi-directional RNNs and Introduction to Language Modeling
Lecture 41: RNNs continued..
48:31
Lecture 41: RNNs continued..
Lecture 40: Introduction to RNNs
48:57
Lecture 40: Introduction to RNNs
Lecture 39: GloVe -- Global Vectors
51:07
Lecture 39: GloVe -- Global Vectors
Lecture 38: word2vec -- Negative sampling
40:37
Lecture 38: word2vec -- Negative sampling
Lecture 37: Neural Network Architecture for word2vec
54:49
Lecture 37: Neural Network Architecture for word2vec
Lecture 36: Introduction to word2vec
47:57
Lecture 36: Introduction to word2vec
Lecture 35: Preparing text as input to neural networks
44:30
Lecture 35: Preparing text as input to neural networks
Lecture 34: Introduction to sequence to sequence.
47:01
Lecture 34: Introduction to sequence to sequence.
Lecture 33: Concluding face detection
30:05
Lecture 33: Concluding face detection
Lecture 32: Face recognition -- Triplet Loss
45:34
Lecture 32: Face recognition -- Triplet Loss
Lecture 31: Introduction to face detection
40:57
Lecture 31: Introduction to face detection
Lecture 30: YOLO - Nonmax Suppression
47:03
Lecture 30: YOLO - Nonmax Suppression
Lecture 29: Introduction to YOLO for object detection.
44:26
Lecture 29: Introduction to YOLO for object detection.
Lecture 28: Sliding windows for object detection
49:24
Lecture 28: Sliding windows for object detection
Lecture 27: Introduction to object detection
49:41
Lecture 27: Introduction to object detection
Lecture 26: MobileNet version 1 and version 2
43:46
Lecture 26: MobileNet version 1 and version 2
Lecture 25: ResNet continued and Inception Network
44:27
Lecture 25: ResNet continued and Inception Network
Lecture 24: ResNet
52:15
Lecture 24: ResNet
Lecture 23: Sparsity of Connections and Parameter sharing in CNNs
45:52
Lecture 23: Sparsity of Connections and Parameter sharing in CNNs
Lecture 22: Putting it all together -- Basic CNNs
46:26
Lecture 22: Putting it all together -- Basic CNNs
Lecture 21: Introduction to CNNs (Continued..)
58:23
Lecture 21: Introduction to CNNs (Continued..)