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)