Posted on 2019-01-02, by nokia241186.
Practical Deep Learning with Keras and Python
.MP4, AVC, 300 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 3h 26m | 651 MB
Instructor: Dr. Mohammad Nauman
Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training
This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have tried to use a machine learning course but could never figure out how to use it to solve your own problems.
In this course, we will start from scratch. So we will immediately start coding even before installation! You will see a brief bit of absolutely essential theory and then we will get into environment setup and explain almost all concepts through code. You will be using Keras, one of the easiest and most powerful machine learning tools out there.
You will start with a basic model of how machines learn and then move on to higher models, such as:
Convolutional Neural Networks
Google's Inception Module
All this with only a few lines of code. All the examples used in the course come with starter code which will get you started and without the hard work.
What You Will Learn
Basics of machine learning with minimal math
A specialized but optional math-heavy discussion that explains all the inner working of machine learning and deep learning
Applying machine learning principles to solve a real-world case study that includes pre-processing and getting your data into the proper shape. (This case study comes from real research work I have carried out recently.)
Understand the often problematic shape issue that makes machine learning difficult to apply in real life
Learn the details of ConvNets and graph-based machine learning models such as Residual Connections and Google's Inception Module
Use Keras' functional API to create powerful models that will help you move way beyond the contents covered in this course
Learn how to use Google's GPUs to speed up your experiments for free
Tips on avoiding mistakes made by newcomers to the field and best practices to get you to your goal with minimal effort
(Buy premium account for maximum speed and resuming ability)
- Ebooks list page : 38400
- 2018-12-31Practical Deep Learning with Keras and Python
- 2018-12-31Packt Practical Deep Learning with Keras and Python-RiDWARE
- 2021-06-24Packt Sentiment Analysis through Deep Learning with Keras and Python-ZH
- 2020-01-28Packt Sentiment Analysis through Deep Learning with Keras and Python-ZH
- 2019-10-29Sentiment Analysis through Deep Learning with Keras and Python
- 2019-01-26PACKT- Practical Deep Learning with Keras and Pyt hon RiDWARE
- 2020-05-30Deep Learning With Keras And Tensorflow In Python And R
- 2020-04-01Deep Learning with Keras and Tensorflow in Python and R
- 2020-03-31Deep Learning with Keras and Tensorflow in Python and R
- 2019-11-22Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch
- 2019-11-07Beginning Anomaly Detection Using Python Based Deep Learning With Keras and PyTorch
- 2019-10-17Beginning Anomaly Detection Using Python-Based Deep Learning: With Keras and PyTorch
- 2019-01-15Deep Learning 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python
- 2019-01-07Deep Learning 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python
- 2020-03-01Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More
- 2019-01-15Practical Machine Learning with R and Python Third Edition Machine Learning in Stereo
- 2019-01-11Practical Machine Learning with R and Python Third Edition Machine Learning in Stereo
- 2019-01-06Practical Machine Learning with R and Python Third Edition Machine Learning in Stereo
- 2017-12-15Practical Machine Learning with R and Python; Machine Learning in Stereo
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