种子简介

种子名称： [GigaCourse.Com] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide
文件类型： 视频
文件数目： 76个文件
文件大小： 5.49 GB
收录时间： 2023-1-30 23:35
已经下载： 3次
资源热度： 233
最近下载： 2024-11-20 14:40

下载BT种子文件

下载Torrent文件(.torrent) 立即下载

磁力链接下载

magnet:?xt=urn:btih:05d2530884902d1f7c4767f060c4ae6083c50433&dn=[GigaCourse.Com] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide 复制链接到迅雷、QQ旋风进行下载，或者使用百度云离线下载。

喜欢这个种子的人也喜欢

种子包含的文件

[GigaCourse.Com] Udemy - Deep Learning using Keras - Complete Compact Dummies Guide.torrent

01 Course Introduction and Table of Contents/001 Course Introduction and Table of Contents.mp4255.18MB

02 Introduction to AI and Machine Learning/001 Introduction to AI and Machine Learning.mp447.45MB

03 Introduction to Deep learning and Neural Networks/001 Introduction to Deep learning and Neural Networks.mp487.53MB

04 Setting up Computer - Installing Anaconda/001 Setting up Computer - Installing Anaconda.mp485.57MB

05 Python Basics/001 Python Basics - Assignment.mp463.43MB

05 Python Basics/002 Python Basics - Flow Control - Part 1.mp446.83MB

05 Python Basics/003 Python Basics - Flow Control - Part 2.mp436.43MB

05 Python Basics/004 Python Basics - List and Tuples.mp446.08MB

05 Python Basics/005 Python Basics - Dictionary and Functions - part 1.mp453.6MB

05 Python Basics/006 Python Basics - Dictionary and Functions - part 2.mp433.93MB

06 Numpy Basics/001 Numpy Basics - Part 1.mp441.01MB

06 Numpy Basics/002 Numpy Basics - Part 2.mp452.78MB

07 Matplotlib Basics/001 Matplotlib Basics - part 1.mp451.23MB

07 Matplotlib Basics/002 Matplotlib Basics - part 2.mp437.99MB

08 Pandas Basics/001 Pandas Basics - Part 1.mp458.6MB

08 Pandas Basics/002 Pandas Basics - Part 2.mp433.57MB

09 Installing Deep Learning Libraries/001 Installing Deep Learning Libraries.mp452.79MB

10 Basic Structure of Artificial Neuron and Neural Network/001 Basic Structure of Artificial Neuron and Neural Network.mp463MB

11 Activation Functions Introduction/001 Activation Functions Introduction.mp449.3MB

12 Popular Types of Activation Functions/001 Popular Types of Activation Functions.mp479.19MB

13 Popular Types of Loss Functions/001 Popular Types of Loss Functions.mp486.75MB

14 Popular Optimizers/001 Popular Optimizers.mp488.35MB

15 Popular Neural Network Types/001 Popular Neural Network Types.mp489.15MB

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.mp499.73MB

17 Step 2 and 3 EDA and Data Preparation/001 Step 2 and 3 EDA and Data Preparation - Part 1.mp4149.77MB

17 Step 2 and 3 EDA and Data Preparation/002 Step 2 and 3 EDA and Data Preparation - Part 2.mp4120.41MB

18 Step 4 Defining the Keras Model/001 Step 4 Defining the Keras Model - Part 1.mp458.17MB

18 Step 4 Defining the Keras Model/002 Step 4 Defining the Keras Model - Part 2.mp464.54MB

19 Step 5 and 6 Compile and Fit Model/001 Step 5 and 6 Compile and Fit Model.mp4110.25MB

20 Step 7 Visualize Training and Metrics/001 Step 7 Visualize Training and Metrics.mp483.53MB

21 Step 8 Prediction Using the Model/001 Step 8 Prediction Using the Model.mp448.13MB

22 Heart Disease Binary Classification Model - Introduction/001 Heart Disease Binary Classification Model - Introduction.mp453.05MB

23 Step 1 - Fetch and Load Data/001 Step 1 - Fetch and Load Data.mp485.89MB

24 Step 2 and 3 - EDA and Data Preparation/001 Step 2 and 3 - EDA and Data Preparation - Part 1.mp469.1MB

24 Step 2 and 3 - EDA and Data Preparation/002 Step 2 and 3 - EDA and Data Preparation - Part 2.mp476.19MB

25 Step 4 - Defining the model/001 Step 4 - Defining the model.mp465.42MB

26 Step 5 - Compile Fit and Plot the Model/001 Step 5 - Compile Fit and Plot the Model.mp474.42MB

27 Step 5 - Predicting Heart Disease using Model/001 Step 5 - Predicting Heart Disease using Model.mp450.06MB

28 Redwine Quality MultiClass Classification Model - Introduction/001 Redwine Quality MultiClass Classification Model - Introduction.mp437.11MB

29 Step1 - Fetch and Load Data/001 Step1 - Fetch and Load Data.mp446.01MB

30 Step 2 - EDA and Data Visualization/001 Step 2 - EDA and Data Visualization.mp4101.08MB

31 Step 3 - Defining the Model/001 Step 3 - Defining the Model.mp472.82MB

32 Step 4 - Compile Fit and Plot the Model/001 Step 4 - Compile Fit and Plot the Model.mp478.17MB

33 Step 5 - Predicting Wine Quality using Model/001 Step 5 - Predicting Wine Quality using Model.mp442.02MB

34 Serialize and Save Trained Model for Later Use/001 Serialize and Save Trained Model for Later Use.mp449.14MB

35 Digital Image Basics/001 Digital Image Basics.mp483.91MB

36 Basic Image Processing using Keras Functions/001 Basic Image Processing using Keras Functions - Part 1.mp462.65MB

36 Basic Image Processing using Keras Functions/002 Basic Image Processing using Keras Functions - Part 2.mp465.45MB

36 Basic Image Processing using Keras Functions/003 Basic Image Processing using Keras Functions - Part 3.mp446.44MB

37 Keras Single Image Augmentation/001 Keras Single Image Augmentation - Part 1.mp4104.04MB

37 Keras Single Image Augmentation/002 Keras Single Image Augmentation - Part 2.mp495.03MB

38 Keras Directory Image Augmentation/001 Keras Directory Image Augmentation.mp4105.63MB

39 Keras Data Frame Augmentation/001 Keras Data Frame Augmentation.mp499.1MB

40 CNN Basics/001 CNN Basics.mp4125.52MB

41 Stride Padding and Flattening Concepts of CNN/001 Stride Padding and Flattening Concepts of CNN.mp496.13MB

42 Flowers CNN Image Classification Model - Fetch Load and Prepare Data/001 Flowers CNN Image Classification Model - Fetch Load and Prepare Data.mp492.3MB

43 Flowers Classification CNN - Create Test and Train Folders/001 Flowers Classification CNN - Create Test and Train Folders.mp463.93MB

44 Flowers Classification CNN - Defining the Model/001 Flowers Classification CNN - Defining the Model - Part 1.mp453.57MB

44 Flowers Classification CNN - Defining the Model/002 Flowers Classification CNN - Defining the Model - Part 2.mp489.03MB

44 Flowers Classification CNN - Defining the Model/003 Flowers Classification CNN - Defining the Model - Part 3.mp436.79MB

45 Flowers Classification CNN - Training and Visualization/001 Flowers Classification CNN - Training and Visualization.mp4106.53MB

46 Flowers Classification CNN - Save Model for Later Use/001 Flowers Classification CNN - Save Model for Later Use.mp426.35MB

47 Flowers Classification CNN - Load Saved Model and Predict/001 Flowers Classification CNN - Load Saved Model and Predict.mp469.87MB

48 Flowers Classification CNN - Optimization Techniques - Introduction/001 Flowers Classification CNN - Optimization Techniques - Introduction.mp440.54MB

49 Flowers Classification CNN - Dropout Regularization/001 Flowers Classification CNN - Dropout Regularization.mp469.36MB

50 Flowers Classification CNN - Padding and Filter Optimization/001 Flowers Classification CNN - Padding and Filter Optimization.mp482.87MB

51 Flowers Classification CNN - Augmentation Optimization/001 Flowers Classification CNN - Augmentation Optimization.mp458.59MB

52 Hyper Parameter Tuning/001 Hyper Parameter Tuning - Part 1.mp497.98MB

52 Hyper Parameter Tuning/002 Hyper Parameter Tuning - Part 2.mp4125.61MB

53 Transfer Learning using Pretrained Models - VGG Introduction/001 Transfer Learning using Pretrained Models - VGG Introduction.mp495.91MB

54 VGG16 and VGG19 prediction/001 VGG16 and VGG19 prediction - Part 1.mp4100.73MB

54 VGG16 and VGG19 prediction/002 VGG16 and VGG19 prediction - Part 2.mp446.51MB

55 ResNet50 Prediction/001 ResNet50 Prediction.mp494.23MB

56 VGG16 Transfer Learning Training Flowers Dataset/001 VGG16 Transfer Learning Training Flowers Dataset - part 1.mp476.67MB

56 VGG16 Transfer Learning Training Flowers Dataset/002 VGG16 Transfer Learning Training Flowers Dataset - part 2.mp4106.31MB

57 VGG16 Transfer Learning Flower Prediction/001 VGG16 Transfer Learning Flower Prediction.mp427.48MB