User talk:Shrikarsan/sandbox

- TensorFlow Training: https://www.edureka.co/ai-deep-learni... **

This Edureka TensorFlow Full Course video is a complete guide to Deep Learning using TensorFlow. It covers in-depth knowledge about Deep Leaning, Tensorflow & Neural Networks. 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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Following topics are covered in this video: 02:27 History Of AI 06:45 Demand For AI 08:46 What Is Artificial Intelligence? 09:50 AI Applications 16:49 Types Of AI 20:24 Programming Languages For AI 27:12 Introduction To Machine Learning 28:08 Need For Machine Learning 31:48 What Is Machine Learning? 34:13 Machine Learning Definitions 37:26 Machine Learning Process 49:13 Types Of Machine Learning 49:21 Supervised Learning 52:00 Unsupervised Learning 53:44 Reinforcement Learning 55:29 Supervised vs Unsupervised vs Reinforcement Learning 58:23 Types Of Problems Solved Using Machine Learning 1:04:49 Supervised Learning Algorithms 1:05:17 Linear Regression 1:11:20 Linear Regression Demo 1:26:36 Logistic Regression 1:35:36 Decision Tree 1:55:18 Random Forest 2:07:31 Naive Bayes 2:14:37 K Nearest Neighbour (KNN) 2:20:31 Support Vector Machine (SVM) 2:26:40 Demo (Classification Algorithms) 2:42:36 Unsupervised Learning Algorithms 2:42:45 K-means Clustering 2:50:49 Demo (Unsupervised Learning) 2:56:40 Reinforcement Learning 3:24:36 Demo (Reinforcement Learning) 3:31:41 AI vs Machine Learning vs Deep Learning 3:33:08 Limitations Of Machine Learning 3:36:32 Introduction To Deep Learning 3:38:36 How Deep Learning Works? 3:40:48 What Is Deep Learning? 3:41:50 Deep Learning Use Case 3:43:14 Single Layer Perceptron 3:50:56 Multi Layer Perceptron (ANN) 3:52:55 Backpropagation 3:54:39 Training A Neural Network 4:01:02 Limitations Of Feed Forward Network 4:03:18 Recurrent Neural Networks 4:05:36 Convolutional Neural Networks 4:09:00 Demo (Deep Learning) 4:29:02 Natural Language Processing 4:30:53 What Is Text Mining? 4:32:43 What Is NLP? 4:33:26 Applications Of NLP 4:35:53 Terminologies In NLP 4:41:19 NLP Demo 4:47:21 Machine Learning Masters Program — Preceding unsigned comment added by Shrikarsan (talk • contribs) 19:44, 20 August 2019 (UTC)Reply

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