Deep Learning Full Course - Learn Deep Learning in 6 Hours | Deep Learning Tutorial | Edureka

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This Edureka Deep Learning Full Course video will help you understand and learn Deep Learning & Tensorflow in detail. This Deep Learning Tutorial is ideal for both beginners as well as professionals who want to master Deep Learning Algorithms. Below are the topics covered in this Deep Learning tutorial video:
00:00 Introduction
3:11 What is Deep Learning
3:55 Why Artificial Intelligence?
5:48 What is AI?
6:53 Applications of AI
8:43 Machine Learning
10:28 Types of Machine Learning
10:33 Supervised Learning
11:43 Unsupervised Learning
13:08 Reinforcement Learning
14:38 Limitations of Machine Learning
16:08 Deep Learning to the Rescue
19:28 What is Deep Learning?
22:58 Deep Learning Example
24:28 Deep Learning Applications
25:48 Deep Learning Tutorial
27:08 Understanding Deep Learning With an Analogy
29:58 How Deep Learning works?
31:12 Why We need Artificial Neuron?
32:58 Perceptron Learning Algorithm
36:13 Types of Activation Functions
41:33 Single Layer Perceptron Use-case
42:33 What is TensorFlow?
44:18 Tensorflow Code Basics
49:08 TensorFlow Example
59:13 What is a Computational Graph?
1:27:08 Limitations of Single Layer Perceptron
1:28:08 Multilayer Perceptron
1:29:18 How it works?
1:29:23 What is Backpropagation?
1:30:23 Backpropagation Learning Algorithm
1:34:43 Multilayer Perceptron Use-case
1:37:48 Top 8 Deep Learning Frameworks
1:38:18 Chainer
1:39:18 CNTK
1:40:48 Caffe
1:42:28 MXNet
1:43:33 Deeplearning4j
1:45:23 Keras
1:46:58 PyTorch
1:48:23 TensorFlow
1:50:23 TensorFlow Tutorial
1:50:43 Rock or Mine Prediction Use-case
1:52:53 How to Create This Model?
1:54:13 What are Tensors?
1:54:38 Tensor Rank
1:55:58 What is TensorFlow?
2:02:28 Graph Visualization
2:05:10 Constant, Placeholder & Variables
2:08:55 Creating A Model
2:17:06 Reducing The Loss
2:18:31 Batch Gradient Descent
2:22:01 Implementing Rock or Mine Prediction Use-case
2:36:24 Artificial Neural Network Tutorial
2:39:29 Why Neural Network?
2:40:29 Problems Before Neural Network
2:42:09 What is Artificial Neural Network?
2:44:04 How It Works?
2:46:24 Perceptron Learning Algorithm - Beer Analogy
2:52:24 Multilayer Perceptron
2:53:34 Artificial Neutral Network
2:54:24 Training A Neural Network
3:05:54 Applications of Network Networks
3:09:04 Backpropagation & Gradient Descent Tutorial
3:09:49 Perceptron
3:10:44 How does the Network Learn?
3:11:09 MNIST Dataset
3:11:59 Cost Function
3:13:54 Finding Local Minima
3:16:09 Gradient Descent Learning
3:17:19 Back Propagation
3:21:29 Recurrent Neural Networks
3:22:04 Why not Feedforward Network?
3:24:29 What is Recurrent Neural Networks?
3:29:24 Training A Recurrent Neural Network
3:29:49 Vanishing & Exploding Gradient Problem
3:34:09 Long Short Term Memory Networks
3:51:04 Convolutional Neural Network
3:51:29 How A Computer Reads An Image?
3:52:14 Why Not Fully Connected Network?
3:53:29 What Convolutional Neural Network?
3:54:04 How CNN Works?
3:54:39 Convolution Layer
3:59:04 ReLU Layer
4:03:49 Fully Connected Layer
4:11:59 Autoencoders Tutorial
4:13:49 PCA vs Autoencoders
4:15:14 Introduction to Autoencoders
4:17:09 Properties of Autoencoders
4:18:09 Training Autoencoders
4:19:14 Architecture of Autoencoders
4:23:49 Types of Autoencoders
4:25:49 Convolutional Autoencoders
4:26:44 Sparse Autoencoders
4:28:29 Deep Autoencoders
4:30:29 Contractive Autoencoders
4:31:54 Demo
4:35:09 Restricted Boltzmann Machine
4:38:54 Working of RBMs
4:40:29 RBM: Energy-Based Model
4:42:34 RBM: Probabilistic Model
4:42:54 RBM Training
4:44:09 RBM: Training to Prediction
4:44:39 RBM: Example
4:46:29 TensorFlow Object Detection
4:47:34 What is Object Detection?
4:48:24 Object Detection Applications
4:51:04 Workflow of Object Detection
4:52:49 Object Detection in TensorFlow
4:53:59 Object Detection Demo
5:10:44 Creating Chatbots Using Tensorflow
5:12:14 What is Chatbots?
5:12:19 How Does ChatBot Works?
5:14:44 Applications of Chatbot
5:15:54 Layers of Chatbot
5:16:14 Natural Language Processing
5:19:59 Demo
5:21:44 Layers of Chatbot
5:21:59 Deep Learning Interview Questions

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3:11 What is Deep Learning
3:55 Why Artificial Intelligence?
5:48 What is AI?
6:53 Applications of AI
8:43 Machine Learning
10:28 Types of Machine Learning
10:33 Supervised Learning
11:43 Unsupervised Learning
13:08 Reinforcement Learning
14:38 Limitations of Machine Learning
16:08 Deep Learning to the Rescue
19:28 What is Deep Learning?
22:58 Deep Learning Example
24:28 Deep Learning Applications
25:48 Deep Learning Tutorial
27:08 Understanding Deep Learning With an Analogy
29:58 How Deep Learning works?
31:12 Why We need Artificial Neuron?
32:58 Perceptron Learning Algorithm
36:13 Types of Activation Functions
41:33 Single Layer Perceptron Use-case
42:33 What is TensorFlow?
44:18 Tensorflow Code Basics
49:08 TensorFlow Example
59:13 What is a Computational Graph?
1:27:08 Limitations of Single Layer Perceptron
1:28:08 Multilayer Perceptron
1:29:18 How it works?
1:29:23 What is Backpropagation?
1:30:23 Backpropagation Learning Algorithm
1:34:43 Multilayer Perceptron Use-case
1:37:48 Top 8 Deep Learning Frameworks
1:38:18 Chainer
1:39:18 CNTK
1:40:48 Caffe
1:42:28 MXNet
1:43:33 Deeplearning4j
1:45:23 Keras
1:46:58 PyTorch
1:48:23 TensorFlow
1:50:23 TensorFlow Tutorial
1:50:43 Rock or Mine Prediction Use-case
1:52:53 How to Create This Model?
1:54:13 What are Tensors?
1:54:38 Tensor Rank
1:55:58 What is TensorFlow?
2:02:28 Graph Visualization
2:05:10 Constant, Placeholder & Variables
2:08:55 Creating A Model
2:17:06 Reducing The Loss
2:18:31 Batch Gradient Descent
2:22:01 Implementing Rock or Mine Prediction Use-case
2:36:24 Artificial Neural Network Tutorial
2:39:29 Why Neural Network?
2:40:29 Problems Before Neural Network
2:42:09 What is Artificial Neural Network?
2:44:04 How It Works?
2:46:24 Perceptron Learning Algorithm - Beer Analogy
2:52:24 Multilayer Perceptron
2:53:34 Artificial Neutral Network
2:54:24 Training A Neural Network
3:05:54 Applications of Network Networks
3:09:04 Backpropagation & Gradient Descent Tutorial
3:09:49 Perceptron
3:10:44 How does the Network Learn?
3:11:09 MNIST Dataset
3:11:59 Cost Function
3:13:54 Finding Local Minima
3:16:09 Gradient Descent Learning
3:17:19 Back Propagation
3:21:29 Recurrent Neural Networks
3:22:04 Why not Feedforward Network?
3:24:29 What is Recurrent Neural Networks?
3:29:24 Training A Recurrent Neural Network
3:29:49 Vanishing & Exploding Gradient Problem
3:34:09 Long Short Term Memory Networks
3:51:04 Convolutional Neural Network
3:51:29 How A Computer Reads An Image?
3:52:14 Why Not Fully Connected Network?
3:53:29 What Convolutional Neural Network?
3:54:04 How CNN Works?
3:54:39 Convolution Layer
3:59:04 ReLU Layer
4:03:49 Fully Connected Layer
4:11:59 Autoencoders Tutorial
4:13:49 PCA vs Autoencoders
4:15:14 Introduction to Autoencoders
4:17:09 Properties of Autoencoders
4:18:09 Training Autoencoders
4:19:14 Architecture of Autoencoders
4:23:49 Types of Autoencoders
4:25:49 Convolutional Autoencoders
4:26:44 Sparse Autoencoders
4:28:29 Deep Autoencoders
4:30:29 Contractive Autoencoders
4:31:54 Demo
4:35:09 Restricted Boltzmann Machine
4:38:54 Working of RBMs
4:40:29 RBM: Energy-Based Model
4:42:34 RBM: Probabilistic Model
4:42:54 RBM Training
4:44:09 RBM: Training to Prediction
4:44:39 RBM: Example
4:46:29 TensorFlow Object Detection
4:47:34 What is Object Detection?
4:48:24 Object Detection Applications
4:51:04 Workflow of Object Detection
4:52:49 Object Detection in TensorFlow
4:53:59 Object Detection Demo
5:10:44 Creating Chatbots Using Tensorflow
5:12:14 What is Chatbots?
5:12:19 How Does ChatBot Works?
5:14:44 Applications of Chatbot
5:15:54 Layers of Chatbot
5:16:14 Natural Language Processing
5:19:59 Demo
5:21:44 Layers of Chatbot
5:21:59 Deep Learning Interview Questions

edurekaIN
Автор

Enjoy


00:00 Introduction
3:11 What is Deep Learning
3:55 Why Artificial Intelligence?
5:48 What is AI?
6:53 Applications of AI
8:43 Machine Learning
10:28 Types of Machine Learning
10:33 Supervised Learning
11:43 Unsupervised Learning
13:08 Reinforcement Learning
14:38 Limitations of Machine Learning
16:08 Deep Learning to the Rescue
19:28 What is Deep Learning?
22:58 Deep Learning Example
24:28 Deep Learning Applications
25:48 Deep Learning Tutorial
27:08 Understanding Deep Learning With an Analogy
29:58 How Deep Learning works?
31:12 Why We need Artificial Neuron?
32:58 Perceptron Learning Algorithm
36:13 Types of Activation Functions
41:33 Single Layer Perceptron Use-case
42:33 What is TensorFlow?
44:18 Tensorflow Code Basics
49:08 TensorFlow Example
59:13 What is a Computational Graph?
1:27:08 Limitations of Single Layer Perceptron
1:28:08 Multilayer Perceptron
1:29:18 How it works?
1:29:23 What is Backpropagation?
1:30:23 Backpropagation Learning Algorithm
1:34:43 Multilayer Perceptron Use-case
1:37:48 Top 8 Deep Learning Frameworks
1:38:18 Chainer
1:39:18 CNTK
1:40:48 Caffe
1:42:28 MXNet
1:43:33 Deeplearning4j
1:45:23 Keras
1:46:58 PyTorch
1:48:23 TensorFlow
1:50:23 TensorFlow Tutorial
1:50:43 Rock or Mine Prediction Use-case
1:52:53 How to Create This Model?
1:54:13 What are Tensors?
1:54:38 Tensor Rank
1:55:58 What is TensorFlow?
2:02:28 Graph Visualization
2:05:10 Constant, Placeholder & Variables
2:08:55 Creating A Model
2:17:06 Reducing The Loss
2:18:31 Batch Gradient Descent
2:22:01 Implementing Rock or Mine Prediction Use-case
2:36:24 Artificial Neural Network Tutorial
2:39:29 Why Neural Network?
2:40:29 Problems Before Neural Network
2:42:09 What is Artificial Neural Network?
2:44:04 How It Works?
2:46:24 Perceptron Learning Algorithm - Beer Analogy
2:52:24 Multilayer Perceptron
2:53:34 Artificial Neutral Network
2:54:24 Training A Neural Network
3:05:54 Applications of Network Networks
3:09:04 Backpropagation & Gradient Descent Tutorial
3:09:49 Perceptron
3:10:44 How does the Network Learn?
3:11:09 MNIST Dataset
3:11:59 Cost Function
3:13:54 Finding Local Minima
3:16:09 Gradient Descent Learning
3:17:19 Back Propagation
3:21:29 Recurrent Neural Networks
3:22:04 Why not Feedforward Network?
3:24:29 What is Recurrent Neural Networks?
3:29:24 Training A Recurrent Neural Network
3:29:49 Vanishing & Exploding Gradient Problem
3:34:09 Long Short Term Memory Networks
3:51:04 Convolutional Neural Network
3:51:29 How A Computer Reads An Image?
3:52:14 Why Not Fully Connected Network?
3:53:29 What Convolutional Neural Network?
3:54:04 How CNN Works?
3:54:39 Convolution Layer
3:59:04 ReLU Layer
4:03:49 Fully Connected Layer
4:11:59 Autoencoders Tutorial
4:13:49 PCA vs Autoencoders
4:15:14 Introduction to Autoencoders
4:17:09 Properties of Autoencoders
4:18:09 Training Autoencoders
4:19:14 Architecture of Autoencoders
4:23:49 Types of Autoencoders
4:25:49 Convolutional Autoencoders
4:26:44 Sparse Autoencoders
4:28:29 Deep Autoencoders
4:30:29 Contractive Autoencoders
4:31:54 Demo
4:35:09 Restricted Boltzmann Machine
4:38:54 Working of RBMs
4:40:29 RBM: Energy-Based Model
4:42:34 RBM: Probabilistic Model
4:42:54 RBM Training
4:44:09 RBM: Training to Prediction
4:44:39 RBM: Example
4:46:29 TensorFlow Object Detection
4:47:34 What is Object Detection?
4:48:24 Object Detection Applications
4:51:04 Workflow of Object Detection
4:52:49 Object Detection in TensorFlow
4:53:59 Object Detection Demo
5:10:44 Creating Chatbots Using Tensorflow
5:12:14 What is Chatbots?
5:12:19 How Does ChatBot Works?
5:14:44 Applications of Chatbot
5:15:54 Layers of Chatbot
5:16:14 Natural Language Processing
5:19:59 Demo
5:21:44 Layers of Chatbot
5:21:59 Deep Learning Interview Questions

vishwanath-ts
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I love the way the concepts and ideas are explained, really helpful and easy to understand once you have the basic idea and fundamentals of ml

joancamilomina
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Beautifully described all the complex parts in an easiest way...Thank you so much 🙏

piyalikarmakar
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Really, I love you Edureka ! u r doing a Great Job ! All the best !
Extremely helpful, appreciate!
Great Video course! Thanks everyone involved for these presentation!

gaquhswhehsiwejowwj
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I love this channel, so grateful for the instructor

dramese
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This is so great, a push on my final year project, send me the datasets please

mugashabradley
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Great video that simplifies.
I can understand it though I'm not a programmer, probably because I've seen other DL lectures as well, but this is really easy to understand from a beginner's standpoint. Thank you.

rowenab.
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Superb explanation from basics to sky-high. Thanks team edureka !

letstalk
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Thanks a lot edureka.. You have done a great great job.. Understood many concepts of deep learning very clearly

tapanjeetroy
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Really, I love you Edureka ! u r doing a Great Job ! All the best !

veereshsuryac
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Thank you very much team edureka! for giving me good knowledge of deep learning..

AmitSingh-wiiv
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You guys are just amazzing.👌👌
Good work. I love it
Thank you for doing this 🙌🙌👌

tylerterrance
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Thank you so much for designing this course.

hashimhafeez
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great lecture ~! It is really, really helpful

kaienyim
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You guys so much simplified this for me

jimable
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Great Video course! Thanks everyone involved for these presentation!

mahendrapersaud
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Awesome video guys, really you are doing great job....its the best video I have ever seen on this topic

shaarukkhaan
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thanks so much edureka i love you
Thank you for doing this 🙌🙌👌

e.byrdporter
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Thanks for uploading such a good video

PritishMishra