Below are the topics covered in this TensorFlow tutorial:

2:07 Artificial Intelligence

2:21 Why Artificial Intelligence?

5:27 What is Artificial Intelligence?

5:55 Artificial Intelligence Domains

6:14 Artificial Intelligence Subsets

11:17 Machine Learning

12:32 Types of Machine Learning

12:39 Machine Learning Use Case

15:55 Supervised Learning

18:50 Types of Supervised Learning

20:17 Use Case 2

21:28 Linear Regression

26:34 Linear Regression Demo

38:39 Regression Application

40:14 Building Logistic Regression Model

40:24 Logistic Regression Use Case

46:55 Analysing Performance Of The Model
	
49:40 Calculating The Accuracy
	
51:31 Logistic Regression Demo

1:01:38 Clustering Use Case

1:05:12 How Clustering works?

1:05:12 Initialization
	
1:06:07 Cluster Assignment
	
1:07:37 Move Centroid
	
1:08:27 Optimization
	
1:08:32 Convergence
	
1:09:22 How to find optimal solution?
	
1:09:30 Choosing the number of cluster

1:16:35 Reinforcement Learning

1:17:35 Limitation of Machine Learning

1:22:00 How Deep Learning Solves the Issue?

1:25:05 What is Deep Learning?

1:26:35 Applications of Deep Learning

1:29:14 What is a Tensor?

1:29:48 Rank of Tensors

1:32:13 Shape of a Tensor

1:33:58 What is TensorFlow?

1:35:38 TensorFlow Code Basics

1:36:09 TensorFlow Basic Demo

2:00:33 Activation or Transformation Function

2:01:28 Linear
	
2:02:18 Unit Step
	
2:03:23 Sigmoid
	
2:04:23 Tanh
	
2:05:18 ReLU
	
2:05:53 Softmax

2:07:03 Activation Function Demo

2:10:43 How Neuron Works?

2:13:08 What is a Perceptron?

2:15:53 Role of Weights & Bias

2:16:18 Perceptron Example

2:22:23 Training a Perceptron

2:22:48 Perceptron Learning Algorithm

2:26:08 Training Network Weights

2:39:43 Reducing The Loss

2:43:18 Perceptron Learning Algorithm Demo

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