Deep Learning Using Keras - A Complete and Compact Guide for Beginners

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Abhilash Nelson

9:33:38

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01.01-course introduction and table of contents.mp4

15:41

02.01-introduction to ai (artificial intelligence) and machine learning.mp4

04:44

02.02-introduction to deep learning.mp4

06:40

03.01-installing anaconda.mp4

09:56

04.01-assignment.mp4

06:42

04.02-flow control-part 1.mp4

04:48

04.03-flow control-part 2.mp4

04:00

04.04-list and tuples.mp4

04:47

04.05-dictionary and functions-part 1.mp4

05:07

04.06-dictionary and functions-part 2.mp4

03:35

05.01-numpy basics-part 1.mp4

03:55

05.02-numpy basics-part 2.mp4

05:10

06.01-matplotlib basics-part 1.mp4

04:32

06.02-matplotlib basics-part 2.mp4

03:55

07.01-pandas basics-part 1.mp4

05:47

07.02-pandas basics-part 2.mp4

04:16

08.01-installing deep learning libraries.mp4

05:17

09.01-basic structure.mp4

05:52

10.01-introduction.mp4

04:25

11.01-popular types of activation functions.mp4

07:14

12.01-popular types of loss functions.mp4

07:51

13.01-popular optimizers.mp4

07:22

14.01-popular neural network types.mp4

06:34

15.01-step 1-fetch and load dataset.mp4

09:35

15.02-step 2 and 3-eda (exploratory data analysis) and data preparation-part 1.mp4

14:15

15.03-step 2 and 3-eda and data preparation-part 2.mp4

11:51

15.04-step 4-defining the keras model-part 1.mp4

05:15

15.05-step 4-defining the keras model-part 2.mp4

05:45

15.06-step 5 and 6-compile and fit model.mp4

10:17

15.07-step 7-visualize training and metrics.mp4

07:58

15.08-step 8-prediction using the model.mp4

04:46

16.01-heart disease binary classification model-introduction.mp4

04:11

16.02-step 1-fetch and load data.mp4

07:51

16.03-step 2 and 3-eda and data preparation-part 1.mp4

06:57

16.04-step 2 and 3-eda and data preparation-part 2.mp4

07:35

16.05-step 4-defining the model.mp4

06:53

16.06-step 5 and 6-compile fit and plot the model.mp4

06:30

16.07-step 7-predicting heart disease using model.mp4

04:46

17.01-introduction.mp4

03:00

17.02-step 1-fetch and load data.mp4

04:52

17.03-step 2 and 3-eda and data visualization.mp4

11:14

17.04-step 4-defining the model.mp4

07:18

17.05-step 5 and 6-compile fit and plot the model.mp4

07:18

17.06-step 7-predicting wine quality using model.mp4

04:35

17.07-serialize and save trained model for later use.mp4

04:57

18.01-digital image.mp4

07:18

18.02-basic image processing using keras functions-part 1.mp4

06:54

18.03-basic image processing using keras functions-part 2.mp4

06:43

18.04-basic image processing using keras functions-part 3.mp4

04:32

19.01-keras single image augmentation-part 1.mp4

09:53

19.02-keras single image augmentation-part 2.mp4

08:45

19.03-keras directory image augmentation.mp4

10:01

19.04-keras data frame augmentation.mp4

10:00

20.01-cnn (convolutional neural networks) basics.mp4

11:02

20.02-stride padding and flattening concepts of cnn.mp4

08:43

21.01-fetch load and prepare data.mp4

08:46

21.02-create test and train folders.mp4

05:23

21.03-defining the model-part 1.mp4

05:07

21.04-defining the model-part 2.mp4

08:10

21.05-defining the model-part 3.mp4

03:46

21.06-training and visualization.mp4

11:06

21.07-save model for later use.mp4

03:06

21.08-load saved model and predict.mp4

09:10

21.09-improving model-optimization techniques.mp4

03:07

21.10-dropout regularization.mp4

06:24

21.11-padding and filter optimization.mp4

07:49

21.12-augmentation optimization.mp4

05:44

21.13-hyper parameter tuning-part 1.mp4

08:12

21.14-hyper parameter tuning-part 2.mp4

13:01

22.01-vgg introduction.mp4

07:56

23.01-vgg16 and vgg19 prediction-part 1.mp4

09:31

23.02-vgg16 and vgg19 prediction-part 2.mp4

04:36

24.01-resnet50 prediction.mp4

07:49

25.01-vgg16-part 1.mp4

07:18

25.02-vgg16-part 2.mp4

10:53

26.01-vgg16 transfer learning flower prediction.mp4

02:42

27.01-preparing and uploading dataset.mp4

08:14

27.02-training and prediction.mp4

21:40

28.01-training and prediction.mp4

07:42

29.01-training and prediction.mp4

06:46

9781803231587 Code.zip

Description

The artificial intelligence domain is divided broadly into deep learning and machine learning. In fact, deep learning is machine learning itself but deep learning with its deep neural networks and algorithms tries to learn high-level features from data without human intervention. That makes deep learning the base of all future self-intelligent systems.

This course begins with going over the basics of Python and then quickly moves on to important libraries of Python that are critical to data analysis and visualizations, such as NumPy, Pandas, and Matplotlib. After the basics, we will then install the deep learning libraries—Theano and TensorFlow—and the API for dealing with these called Keras.

Then, before we jump into deep learning, we will have an elaborate theory session about the basic structure of artificial neuron and neural networks, and about activation functions, loss functions, and optimizers.

Furthermore, we will create deep learning multi-layer neural network models for a text-based dataset and then convolutional neural networks for an image-based dataset.

You will also learn how the basic CNN layers such as the convolution layer, the pooling layer, and the fully connected layer work. Then, we will use different techniques to improve the quality of a model and perform optimization using image augmentation.

By the end of this course, you will have a complete understanding of deep learning and will be able to implement these skills in your own projects.

The complete code bundle for this course is available at https://github.com/PacktPublishing/Deep-Learning-using-Keras---A-Complete-and-Compact-Guide-for-Beginners

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Deep Learning

Abhilash Nelson

Instructor's Courses

Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.

PacktPub

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Packt is a publishing company founded in 2003 headquartered in Birmingham, UK, with offices in Mumbai, India. Packt primarily publishes print and electronic books and videos relating to information technology, including programming, web design, data analysis and hardware.