Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
19 Hour(s) 12 Minute(s)
Language
English
Taught by
Jose Portilla, Pierian Training
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Learn to use Python for Deep Learning with Google's latest Tensorflow 2 library and Keras!
This course will guide you through how to use Google's latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow 2 framework in a way that is easy to understand.
We'll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0's official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!
This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
NumPy Crash Course
Pandas Data Analysis Crash Course
Data Visualization Crash Course
Neural Network Basics
TensorFlow Basics
Keras Syntax Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
AutoEncoders
GANs - Generative Adversarial Networks
Deploying TensorFlow into Production
and much more!
Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.
TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance
It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!
Become a deep learning guru today! We'll see you inside the course!
Course Content
- 13 section(s)
- 116 lecture(s)
- Section 1 Course Overview, Installs, and Setup
- Section 2 COURSE OVERVIEW CONFIRMATION
- Section 3 NumPy Crash Course
- Section 4 Pandas Crash Course
- Section 5 Visualization Crash Course
- Section 6 Machine Learning Concepts Overview
- Section 7 Basic Artificial Neural Networks - ANNs
- Section 8 Convolutional Neural Networks - CNNs
- Section 9 Recurrent Neural Networks - RNNs
- Section 10 Natural Language Processing
- Section 11 AutoEncoders
- Section 12 Generative Adversarial Networks
- Section 13 Deployment
What You’ll Learn
- Learn to use TensorFlow 2.0 for Deep Learning
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Perform Image Classification with Convolutional Neural Networks
- Use Deep Learning for medical imaging
- Forecast Time Series data with Recurrent Neural Networks
- Use Generative Adversarial Networks (GANs) to generate images
- Use deep learning for style transfer
- Generate text with RNNs and Natural Language Processing
- Serve Tensorflow Models through an API
- Use GPUs for accelerated deep learning
Skills covered in this course
Reviews
-
AAtul Nanal
Basic TF syntax and approach to model solving is explained well...
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RRitika Lochab
nice detailed explanation
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JJason
Very good for people with some basic programming knowledge! If you're using more modern versions of these libraries. There will be some changes that you will need to use AI to understand why these errors are happening to solve them
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PPatrick H Wyant
Too compressed - assumed prior experience with each application. Still, very enlightening to see the possibilities. Now can focus on the appropriate methods for my project. Thank you.
