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[FreeCourseSite.com] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide

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0. Websites you may like/[CourseClub.ME].url
122 B
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133 B
0. Websites you may like/[FreeCourseSite.com].url
127 B
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49 B
01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4
255 MB
02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp4
47 MB
03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp4
88 MB
04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp4
86 MB
05 Python Basics/001 Python Basics - Assignment.mp4
63 MB
05 Python Basics/002 Python Basics - Flow Control - Part 1.mp4
47 MB
05 Python Basics/003 Python Basics - Flow Control - Part 2.mp4
36 MB
05 Python Basics/004 Python Basics - List and Tuples.mp4
46 MB
05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp4
54 MB
05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp4
34 MB
06 Numpy Basics/001 Numpy Basics - Part 1.mp4
41 MB
06 Numpy Basics/002 Numpy Basics - Part 2.mp4
53 MB
07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp4
51 MB
07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp4
38 MB
08 Pandas Basics/001 Pandas Basics - Part 1.mp4
59 MB
08 Pandas Basics/002 Pandas Basics - Part 2.mp4
34 MB
09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp4
53 MB
10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp4
63 MB
11 Activation Functions Introduction/001 Activation Functions Introduction.mp4
49 MB
12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp4
79 MB
13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp4
87 MB
14 Popular Optimizers/001 Popular Optimizers.mp4
88 MB
15 Popular Neural Network Types/001 Popular Neural Network Types.mp4
89 MB
16 King County House Sales Regression Model - Step 1 Fetch and Load Dataset/001 King County House Sales Regression Model - Step 1 Fetch and Load Dataset.mp4
100 MB
17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4
150 MB
17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4
120 MB
18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp4
58 MB
18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp4
64 MB
19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4
110 MB
20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp4
84 MB
21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp4
48 MB
22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp4
53 MB
23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp4
86 MB
24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp4
69 MB
24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp4
76 MB
25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp4
65 MB
26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp4
74 MB
27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp4
50 MB
28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp4
37 MB
29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp4
46 MB
30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4
101 MB
31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp4
73 MB
32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp4
78 MB
33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp4
42 MB
34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp4
49 MB
35 Digital Image Basics/001 Digital Image Basics.mp4
84 MB
36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp4
63 MB
36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp4
65 MB
36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp4
46 MB
37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4
104 MB
37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp4
95 MB
38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4
106 MB
39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp4
99 MB
40 CNN Basics/001 CNN Basics.mp4
126 MB
41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp4
96 MB
42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp4
92 MB
43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp4
64 MB
44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp4
54 MB
44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp4
89 MB
44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp4
37 MB
45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4
106 MB
46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp4
26 MB
47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp4
70 MB
48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp4
40 MB
49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp4
69 MB
50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp4
83 MB
51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp4
59 MB
52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp4
98 MB
52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4
126 MB
53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp4
96 MB
54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4
101 MB
54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp4
46 MB
55 ResNet50 Prediction/001 ResNet50 Prediction.mp4
94 MB
56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp4
77 MB
56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4
106 MB
57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp4
28 MB
58 SOURCE CODE AND FILES ATTACHED/001 SOURCE CODE AND FILES ATTACHED.html
1.1 kB