What you would learn in The Complete Recurrent Neural Network with Python Course course?
Are you interested in Machine Learning, Deep Learning, or Artificial Intelligence? This course is perfect for you!
This course was designed by an engineer in the field of software. I'm hoping that with the expertise and experience I accumulate over my career, I will impart my knowledge and assist you in learning complex concepts algorithmic, programming, and libraries in a way that is easy to understand.
I will guide you step-by-step through Deep Learning. Each time you take a course, you'll learn new abilities and enhance your knowledge of this challenging but lucrative sub-field that is Data Science.
This course is enjoyable and exciting; however, simultaneously, we get deep into Recurrent Neural networks. In the new edition of this course, we will cover numerous tools and technologies, which include:
Deep Learning.
Google Colab
Keras.
Matplotlib.
Separating Data into Test and Training Set.
Training Neural Network.
Model building.
Analyzing Results.
Model compilation.
Create a Prediction.
Accuracy of Testing.
Confusion Matrix.
ROC Curve.
Analysis of text.
Analysis of images.
Layers embedded with embedding.
Word embedding.
Long-short-term memory (LSTM) models.
Sequence-to-vector models.
Vector-to-sequence models.
Bi-directional LSTM.
Sequence-to-sequence models.
Converting the words in feature vectors.
Frequency-inverse document frequency.
Cleaning textual information.
Making of documents to tokens.
Topic modeling using latent Dirichlet allocation
Text documents can be decomposed using LDA.
Autoencoder.
Numpy.
Pandas.
Tensorflow.
Sentiment Analysis.
Matplotlib.
Out-of-core learning.
Bi-directional LSTM.
Additionally, this course is packed with exercises built on real-world examples. This means that you will not only be taught the basics and concepts, but you'll get hands-on experience making the models of your choice. Many projects allow you to try out and improve your skills. These are the projects that are listed below:
Course Content:
- Text analysis
- Image analysis
- Layers of Embedding
- Word embedding
- Models of memory that are long-short-term
- Sequence-to-vector models
- Models of vector-to-sequence
- Bi-directional LSTM
- Sequence-to-sequence models
- Converting the words of a sentence into features vectors
- frequency-inverse document frequency
- Cleaning textual information
- Making documents into tokens
- Topic modeling using latent Dirichlet allocation
- Text documents are decomposed using LDA
- Autoencoder
- Numpy
- Pandas
- Tensorflow
- Sentiment Analysis
- Matplotlib
- Out-of-core learning
Download The Complete Recurrent Neural Network with Python Course from below links NOW!
