Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs

Sequence prediction course that covers topics such as: RNN, LSTM, GRU, NLP, Seq2Seq, Attention, Time series prediction
Rating: 4.1 out of 5 (156 ratings)
12,634 students
Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs
Rating: 4.1 out of 5 (156 ratings)
12,634 students
RNN
LSTM
GRU
NLP
Seq2Seq
Attention
Time series

Requirements

  • Python
  • Numpy
  • Tensorflow or keras
  • Feed Forward Neural Networks
  • Back Propagation
Description

This is a preview to the exciting Recurrent Neural Networks course that will be going live soon. Recurrent Networks are an exciting type of neural network that deal with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs. And dealing with them requires some type of memory element to remember the history of the sequences, this is where Recurrent Neural networks come in.


We will be covering topics such as RNNs, LSTMs, GRUs, NLP, Seq2Seq, attention networks and much much more.


You will also be building projects, such as a Time series Prediction, music generator, language translation, image captioning, spam detection, action recognition and much more.


Building these projects will impress even the most senior machine learning developers; and will prepare you to start tackling your own deep learning projects with real datasets to show off to your colleagues or even potential employers.


Sequential Networks are very exciting to work with and allow for the creation of very intelligent applications. If you’re interested in taking your machine learning skills to the next level, then this course is for you!

Who this course is for:
  • machine learning developers
  • Data Scientiests
Course content
3 sections • 16 lectures • 1h 14m total length
  • Introduction - Please watch
    01:05
  • Additional FREE Content
    00:05
  • Information on the Full Course
    00:03
  • Introduction to Recurrent Neural Networks
    06:19
  • Back propogation through time
    06:14
  • Project Overview
    00:53
  • Preparing our data
    03:28
  • Training our network
    03:59
  • Final Source Code
    12:57
  • For those having trouble with NumPy
    00:10
  • Overview
    00:48
  • Arrays vs Lists
    12:03
  • Multidimensional arrays
    11:46
  • One Dimensional Slicing
    03:33
  • Reshaping
    03:34
  • Multidimensional Slicing
    07:20

Instructors
Data Science Entrepreneur
Jad Slim
  • 4.5 Instructor Rating
  • 9,558 Reviews
  • 83,714 Students
  • 6 Courses

Jad studied mechanical engineering at the University of Ottawa. Jad also has experience in machine learning, computer vision ,mathematical modeling, computer simulation and intelligent systems. He has also developed many deep learning applications, and is currently pursuing an interest in autonomous machines.

Skilled in deep learning libraries such as Tensorflow, Keras and MATLAB.


Teacher
Rayan Slim
  • 4.5 Instructor Rating
  • 9,558 Reviews
  • 95,255 Students
  • 6 Courses

Hi! I'm Rayan, a full time software developer based in Ottawa, Canada.

I first ventured into development when working on a start-up. Since then, I've built countless web and mobile applications as a freelance developer, meanwhile expanding my repertoire and exploring new avenues in Deep Learning & Data Analytics.

In my free time, I love to teach!