[FreeCourseSite.com] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide
File List
- 01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4 255.2 MB
- 17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4 149.8 MB
- 52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4 125.6 MB
- 40 CNN Basics/001 CNN Basics.mp4 125.5 MB
- 17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4 120.4 MB
- 19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4 110.2 MB
- 45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4 106.5 MB
- 56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4 106.3 MB
- 38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4 105.6 MB
- 37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4 104.0 MB
- 30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4 101.1 MB
- 54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4 100.7 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 99.7 MB
- 39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp4 99.1 MB
- 52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp4 98.0 MB
- 41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp4 96.1 MB
- 53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp4 95.9 MB
- 37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp4 95.0 MB
- 55 ResNet50 Prediction/001 ResNet50 Prediction.mp4 94.2 MB
- 42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp4 92.3 MB
- 15 Popular Neural Network Types/001 Popular Neural Network Types.mp4 89.1 MB
- 44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp4 89.0 MB
- 14 Popular Optimizers/001 Popular Optimizers.mp4 88.4 MB
- 03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp4 87.5 MB
- 13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp4 86.8 MB
- 23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp4 85.9 MB
- 04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp4 85.6 MB
- 35 Digital Image Basics/001 Digital Image Basics.mp4 83.9 MB
- 20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp4 83.5 MB
- 50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp4 82.9 MB
- 12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp4 79.2 MB
- 32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp4 78.2 MB
- 56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp4 76.7 MB
- 24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp4 76.2 MB
- 26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp4 74.4 MB
- 31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp4 72.8 MB
- 47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp4 69.9 MB
- 49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp4 69.4 MB
- 24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp4 69.1 MB
- 36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp4 65.4 MB
- 25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp4 65.4 MB
- 18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp4 64.5 MB
- 43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp4 63.9 MB
- 05 Python Basics/001 Python Basics - Assignment.mp4 63.4 MB
- 10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp4 63.0 MB
- 36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp4 62.7 MB
- 08 Pandas Basics/001 Pandas Basics - Part 1.mp4 58.6 MB
- 51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp4 58.6 MB
- 18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp4 58.2 MB
- 05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp4 53.6 MB
- 44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp4 53.6 MB
- 22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp4 53.0 MB
- 09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp4 52.8 MB
- 06 Numpy Basics/002 Numpy Basics - Part 2.mp4 52.8 MB
- 07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp4 51.2 MB
- 27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp4 50.1 MB
- 11 Activation Functions Introduction/001 Activation Functions Introduction.mp4 49.3 MB
- 34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp4 49.1 MB
- 21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp4 48.1 MB
- 02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp4 47.4 MB
- 05 Python Basics/002 Python Basics - Flow Control - Part 1.mp4 46.8 MB
- 54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp4 46.5 MB
- 36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp4 46.4 MB
- 05 Python Basics/004 Python Basics - List and Tuples.mp4 46.1 MB
- 29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp4 46.0 MB
- 33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp4 42.0 MB
- 06 Numpy Basics/001 Numpy Basics - Part 1.mp4 41.0 MB
- 48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp4 40.5 MB
- 07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp4 38.0 MB
- 28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp4 37.1 MB
- 44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp4 36.8 MB
- 05 Python Basics/003 Python Basics - Flow Control - Part 2.mp4 36.4 MB
- 05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp4 33.9 MB
- 08 Pandas Basics/002 Pandas Basics - Part 2.mp4 33.6 MB
- 57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp4 27.5 MB
- 46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp4 26.4 MB
- 58 SOURCE CODE AND FILES ATTACHED/001 SOURCE CODE AND FILES ATTACHED.html 1.1 KB
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