Download Tensorflow 2 and Keras Deep Learning Bootcamp course. Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras!
Description of Complete Tensorflow 2 and Keras Deep Learning Bootcamp
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 Tensorflow 2 and Keras Deep Learning Bootcamp 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 Tensorflow 2 and Keras Deep Learning Bootcamp 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
- 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 Tensorflow 2 and Keras Deep Learning Bootcamp course!
Who this course is for:
- Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence
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
Its interesting, but I see now that the CV is a huge topic for Deep Learning. I still struggle to find applications in my job of CV technology. Does the set of images have to all be of the same kind and charcateristics?
There are not too many datasets like in the case of regression or classification. Hope my opinion changes during the next topics of the course.
This is great course! This is the second course that I have taken from the same instructor and he is awesome! I wish I knew him sooner so I could have saved a lot of time! the only downside of the course is NLP section I think he doesn’t explain it as well as other sections!
This is a very comprehensive class. I needed an understanding of machine learning for my job, and this course is doing more than that. I really appreciate that both the theoretical and practical aspects of machine learning with TensorFlow are covered in the course.
Fantastic job on those beautifully crafted jupyter notebooks and exercises! The only area where you can improve upon is the NLP section. It feels rushed. But other than that, great job!