**Applied Deep Learning with Keras**

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 9h 02m | 12.9 GB

Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.

Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the course guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.

By the end of this course, you will have the skills you need to use Keras when building high-level deep neural networks.

Learn

- Understand the difference between single-layer and multi-layer neural network models
- Use Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networks
- Apply L1, L2, and dropout regularization to improve the accuracy of your model
- Implement cross-validate using Keras wrappers with scikit-learn
- Understand the limitations of model accuracy

**Introduction to Machine Learning with Keras**

1 Course Overview

2 Installation and Setup

3 Lesson Overview

4 Data Representation

5 Loading a Dataset from the UCI Machine Learning Repository

6 Data Pre-Processing

7 Cleaning the Data

8 Appropriate Representation of the Data

9 Lifecycle of Model Creation

10 Machine Learning Libraries and scikit-learn

11 Keras

12 Model Training

13 Creating a Simple Model

14 Model Tuning

15 Regularization

16 Lesson Summary

**Machine Learning versus Deep Learning**

17 Lesson Overview

18 Introduction to ANNs

19 Linear Transformations

20 Matrix Transposition

21 Introduction to Keras

22 Lesson Summary

**Deep Learning with Keras**

23 Lesson Overview

24 Building Your First Neural Network

25 Gradient Descent for Learning the Parameters

26 Model Evaluation

27 Lesson Summary

**Evaluate Your Model with Cross-Validation using Keras Wrappers**

28 Lesson Overview

29 Cross-Validation

30 Cross-Validation for Deep Learning Models

31 Evaluate Deep Neural Networks with Cross-Validation

32 Model Selection with Cross-validation

33 Write User-Defined Functions to Implement Deep Learning Models with Cross-Validation

34 Lesson Summary

**Improving Model Accuracy**

35 Lesson Overview

36 Regularization

37 L1 and L2 Regularization

38 Dropout Regularization

39 Other Regularization Methods

40 Data Augmentation

41 Hyperparameter Tuning with scikit-learn

42 Lesson Summary

**Model Evaluation**

43 Lesson Overview

44 Accuracy

45 Imbalanced Datasets

46 Confusion Matrix

47 Computing Accuracy and Null Accuracy with Healthcare Data

48 Calculate the ROC and AUC Curves

49 Lesson Summary

**Computer Vision with Convolutional Neural Networks**

50 Lesson Overview

51 Computer Vision

52 Architecture of a CNN

53 Image Augmentation

54 Amending Our Model by Reverting to the Sigmoid Activation Function

55 Changing the Optimizer from Adam to SGD

56 Classifying a New Image

57 Lesson Summary

**Transfer Learning and Pre-trained Models**

58 Lesson Overview

59 Pre-Trained Sets and Transfer Learning

60 Fine Tuning a Pre-Trained Network

61 Classification of Images that are not Present in the ImageNet Database

62 Fine-Tune the VGG16 Model

63 Image Classification with ResNet

64 Lesson Summary

**Sequential Modeling with Recurrent Neural Networks**

65 Lesson Overview

66 Sequential Memory and Sequential Modeling

67 Long Short-Term Memory – LSTM

68 Predict the Trend of Apple’s Stock Price Using an LSTM with 50 Units (Neurons)

69 Predicting the Trend of Apple’s Stock Price Using an LSTM with 100 Units

70 Lesson Summary

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