What you'll learn
- Understand how Neural Networks Work
- Build your own Neural Network from Scratch with Python
- Use TensorFlow for Classification and Regression Tasks
- Use TensorFlow for Image Classification with Convolutional Neural Networks
- Use TensorFlow for Time Series Analysis with Recurrent Neural Networks
- Use TensorFlow for solving Unsupervised Learning Problems with AutoEncoders
- Learn how to conduct Reinforcement Learning with OpenAI Gym
- Create Generative Adversarial Networks with TensorFlow
- Become a Deep Learning Guru!
Welcome to the Full Guide to TensorFlow for Deep Learning with Python!
This Complete Guide to TensorFlow for Deep Learning with Python course will guide you via exactly how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This training course intends to offer you an understandable guide to the intricacies of Google's TensorFlow framework in such a way that it is easy to understand. Other courses and tutorials have tended to keep away from pure tensorflow and rather utilize abstractions that offer the customer much less control. Below we offer a training course that lastly acts as a full guide to using the TensorFlow framework as meant while revealing you the most recent techniques available in deep learning!
This Complete Guide to TensorFlow for Deep Learning with Python course is designed to balance theory and practical application, with full Jupiter notebook guides of code and also simple to reference slides and also notes. We also have plenty of exercises to test your new skills along the road!
This training course covers a range of topics, consisting of
- Neural Network Essentials
- TensorFlow Fundamentals
- Artificial Neural Networks
- Densely Connected Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Reinforcement Learning
- OpenAI Gym
- and also much more!
There are several Deep Learning Frameworks around, so why use TensorFlow?
TensorFlow is an open source software library for numerical computation making use of data flow graphs. Nodes in the graph stand for mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture permits you to release computation to several CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was initially established by researchers as well as designers working with the Google Brain Group within Google's Machine Knowledge research organization for the purposes of carrying out machine learning and also deep neural networks research, however the system is general enough to be relevant in a wide variety of various other domain names also.
It is utilized by significant companies around the world, consisting of Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, as well as naturally, Google!
End up being a machine learning expert today! We'll see you inside the Complete Guide to TensorFlow for Deep Learning with Python course!
Who this training course is for:
- Python trainees eager to learn the latest Deep Learning Techniques with TensorFlow
Created by Jose Portilla
Last updated 4/2020
English [Auto-generated], French [Auto-generated]
Size: 2.27 GB