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Deep Learning: Recurrent Neural Networks with Tensorflow 2

person icon Lazy Programmer

4.3

Deep Learning: Recurrent Neural Networks with Tensorflow 2

Master Machine Learning & Neural Networks for Data Science, Time Series, & NLP

updated on icon Updated on Apr, 2024

language icon Language - English

person icon Lazy Programmer

category icon TensorFlow,Development

Lectures -27

Duration -3.5 hours

4.3

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Course Description

In this self-paced course, you will learn how to use Tensorflow 2 to build recurrent neural networks (RNNs). We'll study the Simple RNN (Elman unit), the GRU, and the LSTM. We'll investigate the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We'll apply RNNs to both time series forecasting and natural language processing (NLP). We'll apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do, and how not to make the same mistakes that most blogs and courses make when predicting stocks. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.

This course is intended for:

  • Anyone interested in deep learning and machine learning
  • Anyone who wants to implement recurrent neural networks in Tensorflow 2

Suggested prerequisites:

  • Decent Python programming skill
  • Know how to build a feedforward ANN in Tensorflow 2
  • Experience with data science libraries like Numpy and Matplotlib

In this course, we will cover:

  • Simple RNNs (Elman unit)
  • GRU (gated recurrent unit)
  • LSTM (long short-term memory unit)
  • time series forecasting
  • stock price predictions and stock return predictions
  • how to apply RNNs to natural language processing (NLP)

Goals

What will you learn in this course:

  • Simple RNNs (Elman unit)
  • GRU (gated recurrent unit)
  • LSTM (long short-term memory unit)
  • time series forecasting
  • stock price predictions and stock return predictions
  • how to apply RNNs to natural language processing (NLP)

Prerequisites

What are the prerequisites for this course?

  • Decent Python programming skill
  • Know how to build a feedforward ANN in Tensorflow 2
  • Experience with data science libraries like Numpy and Matplotlib
Deep Learning: Recurrent Neural Networks with Tensorflow 2

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction and Outline
4 Lectures
  • play icon Introduction 02:45 02:45
  • play icon Outline 07:13 07:13
  • play icon Connect With Me For FREE Data Science & Machine Learning Tutorials 00:59 00:59
  • play icon Resources
Recurrent Neural Networks (RNNs), Time Series, and Sequence Data
19 Lectures
Tutorialspoint
Natural Language Processing (NLP)
4 Lectures
Tutorialspoint

Instructor Details

Lazy Programmer

Lazy Programmer

The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.


Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into academia led him to pursue computer engineering, with a specialized focus on machine learning and pattern recognition. Undeterred by boundaries, he then ventured into the realm of statistics, exploring its applications in financial engineering.


Recognized as a trailblazer in his field, the Lazy Programmer quickly embraced the power of deep learning when it was still in its infancy. As one of the pioneers, he fearlessly embarked on instructing one of the first-ever online courses on deep learning, catapulting him to the forefront of the industry.


Beyond the realm of education, the Lazy Programmer possesses invaluable hands-on experience that has shaped his expertise. His ventures into online advertising and digital media have yielded astounding results, propelling click-through rates and conversion rates to new heights and boosting revenues by millions of dollars at the companies he's worked for. As a full-stack software engineer, he boasts intimate familiarity with an array of backend and web technologies, including Python, Ruby on Rails, C++, Scala, PHP, Javascript, SQL, big data, Spark, and Redis.


While his achievements in the field of data science and machine learning are awe-inspiring, the Lazy Programmer's intellectual curiosity extends far beyond these domains. His fervor for knowledge leads him to explore diverse fields such as drug discovery, bioinformatics, and algorithmic trading. Embracing the challenges and intricacies of these subjects, he strives to unravel their potential and contribute to their development.


With an unwavering commitment to his students and a penchant for simplifying complex concepts, the Lazy Programmer stands as an influential figure in the realm of online education. Through his courses in data science, machine learning, deep learning, and artificial intelligence, he empowers aspiring learners to navigate the intricate landscapes of these disciplines with confidence.


As an author, mentor, and innovator, the Lazy Programmer leaves an indelible mark on the world of data science, machine learning, and beyond. With his ability to demystify the most intricate concepts, he continues to shape the next generation of data scientists and inspires countless individuals to embark on their own intellectual journeys.

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