Build & Train RNNs: Implementing Recurrent Neural Networks
Neural Networks
| Intermediate
- 10 Videos | 52m 23s
- Includes Assessment
- Earns a Badge
Learners will examine the concepts of perception, layers of perception, and backpropagation, and discover how to implement recurrent neural network by using Python, TensorFlow, and Caffe2 in this 10-video course. Begin by taking a look at the essential features and processes of implementing perception and backpropagation in machine learning neural networks. Next, you will compare single-layer perception and multilayer perception and describe the need for layer management. You will learn about the steps involved in building recurrent neural network models; building recurrent neural networks with Python and TensorFlow; implementing long short-term memory (LSTM) by using TensorFlow, and building recurrent neural networks with Caffe2. Caffe is a deep learning framework. Building deep learning language models using Keras—an open source neural network library—will be explored in the final tutorial of the course. The concluding exercise entails implementing recurrent neural networks by using TensorFlow and Caffe2 and building deep learning language models by using Keras.
WHAT YOU WILL LEARN
-
identify the essential features and processes of implementing perception and backpropagation
compare single and multilayer perception and describe the need for layer management
describe the steps involved in building recurrent neural network models
implement recurrent neural network using Python and TensorFlow
implement long short-term memory using TensorFlow -
recognize the capabilities provided by Caffe2 for implementing neural networks
implement recurrent neural network using Caffe2
build deep learning language models using Keras
implement RNN using TensorFlow and Caffe2 and build deep learning language models using Keras
IN THIS COURSE
-
1.Course Overview2m 2sUP NEXT
-
2.Perception and Backpropagation3m 21s
-
3.Single and Multilayer Perception3m 16s
-
4.Building Recurrent Neural Network Models2m 40s
-
5.RNN with Python and TensorFlow8m 22s
-
6.LSTM with TensorFlow9m 53s
-
7.Caffe2 and Neural Network3m 20s
-
8.Implement RNN with Caffe26m 4s
-
9.Deep Learning Language Model with Keras5m 26s
-
10.Exercise: Implement RNN Using TensorFlow and Caffe23m 59s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
Digital badges are yours to keep, forever.
