Hands-On Keras for Machine Learning Engineers

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Mike West

2:17:02

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01.01-introduction.mp4

01:35

01.02-what youll learn in this course.mp4

01:33

01.03-is this course right for you.mp4

00:51

01.04-what is keras.mp4

01:24

02.01-theano.mp4

02:07

02.02-tensorflow.mp4

02:57

02.03-artificial neural network anatomy.mp4

02:57

02.04-deep learning.mp4

00:43

02.05-keras life cycle.mp4

06:11

02.06-keras code anatomy.mp4

02:04

02.07-demo case study on pima indian diabetes dataset load data.mp4

02:53

02.08-demo case study on pima indian diabetes dataset define and compile.mp4

03:01

02.09-demo case study on pima indian diabetes dataset fit and evaluate.mp4

01:46

02.10-performance evaluation on neural networks.mp4

01:31

02.11-demo case study on data segmentation.mp4

03:23

02.12-scikit-learn for general machine learning.mp4

01:18

02.13-evaluate models with cross-validation.mp4

01:25

02.14-grid search deep learning model parameters.mp4

02:17

02.15-demo case study on multiclass classification.mp4

01:30

02.16-demo case study on multiclass classification part 2.mp4

04:22

02.17-demo case study on binary classification.mp4

01:22

02.18-demo case study on binary classification part 2.mp4

02:47

02.19-demo case study on binary classification part 3.mp4

01:35

02.20-demo case study on binary classification part 4.mp4

02:31

02.21-demo case study on regression.mp4

01:12

02.22-demo case study on regression part 2.mp4

02:32

02.23-demo case study on regression part 3.mp4

02:39

03.01-model serialization.mp4

00:52

03.02-save neural network to json.mp4

01:44

03.03-save neural network to yaml.mp4

00:44

03.04-demo case study on checkpointing.mp4

02:36

03.05-demo case study on checkpointing part 2.mp4

01:08

03.06-plotting history.mp4

01:12

03.07-visualize model training history in keras.mp4

01:07

03.08-demo case study on dropping out.mp4

02:02

03.09-demo case study on dropping out part 2.mp4

02:39

03.10-dropout tips.mp4

02:21

03.11-learning rate defined.mp4

02:57

03.12-configure learning rate.mp4

02:07

03.13-demo case study on learning rates.mp4

01:40

03.14-demo case study on learning rates part 2.mp4

00:55

03.15-demo case study on learning rates part 3.mp4

01:18

04.01-convolutional neural networks.mp4

04:01

04.02-demo case study on handwritten digit recognition.mp4

02:15

04.03-demo case study handwritten digit recognition part 2.mp4

02:58

04.04-demo case study on handwritten digit recognition part 3.mp4

02:35

04.05-demo case study on handwritten digit recognition part 4.mp4

01:22

04.06-image augmentation.mp4

01:44

04.07-demo case study on image augmentation.mp4

01:08

04.08-demo case study on image augmentation part 2.mp4

02:36

04.09-image augmentation tips.mp4

01:04

04.10-object recognition.mp4

01:25

04.11-demo case study on object recognition.mp4

02:15

04.12-improving model performance.mp4

01:06

04.13-sentiment analysis in keras.mp4

01:30

04.14-imdb dataset properties.mp4

01:01

04.15-word embedding defined.mp4

01:52

04.16-demo case study on word embedding.mp4

01:56

04.17-demo case study on word embedding part 2.mp4

02:00

05.01-recurrent neural networks.mp4

02:01

05.02-demo case study on time series prediction.mp4

01:26

05.03-demo case study on time series prediction part 2.mp4

03:29

05.04-demo case study on time series prediction part 3.mp4

01:19

05.05-demo case study on time series prediction with lstm.mp4

02:17

05.06-demo case study on time series prediction with lstm part 2.mp4

02:04

05.07-demo case study on time series prediction with lstm part 3.mp4

01:58

05.08-demo case study on sequence classification.mp4

02:32

05.09-demo case study on sequence classification part 2.mp4

01:20

9781803232522 Code.zip

Description

Welcome to hands-on Keras for machine learning engineers. This is a carefully structured course to guide you in your journey to learn deep learning in Python with Keras. Discover the Keras Python library for deep learning and learn the process of developing and evaluating deep learning models using it.

There are two top numerical platforms for developing deep learning models; they are Theano, developed by the University of Montreal, and TensorFlow developed at Google. Both were developed for use in Python and both can be leveraged by the super-simple-to-use Keras library. Keras wraps the numerical computing complexity of Theano and TensorFlow, providing a concise API that we will use to develop our own neural network and deep learning models. Keras has become the gold standard in the applied space for rapid prototyping deep learning models.

This course is a hands-on guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to your own deep learning Keras models.

All resources and code files for this course are placed here: https://github.com/PacktPublishing/Hands-On-Keras-for-Machine-Learning-Engineers

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Machine Learning

Mike West

Instructor's Courses

Mike has Bachelor of Science degrees in Business and Psychology. He started his career as a middle school psychologist prior to moving into the information technology space. His love of computers resulted in him spending many additional hours working on computers while studying for his master's degree in Statistics. His current areas of interests include Machine Learning, Data Engineering and SQL Server. When not working, Mike enjoys spending time with his family and traveling.

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.