Deep Learning: Recurrent Neural Networks in Python

What you'll learn:

• Understand the simple recurrent unit (Elman unit)

• Understand the GRU (gated recurrent unit)

• Understand the LSTM (long short-term memory unit)

• Write various recurrent networks in Theano
• Understand backpropagation through time
• Understand how to mitigate the vanishing gradient problem
• Solve the XOR and parity problems using a recurrent neural network
• Use recurrent neural networks for language modeling
• Use RNNs for generating text, like poetry
• Visualize word embeddings and look for patterns in word vector representations

Requirements:

• Calculus
• Linear algebra
• Python, Numpy, Matplotlib
• Write a neural network in Theano
• Understand backpropagation
• Probability (conditional and joint distributions)
• Write a neural network in Tensorflow

Description:

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

Learn about one of the most powerful Deep Learning architectures yet!

The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.

This includes time series analysis, forecasting and natural language processing (NLP).

Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.

This course will teach you:

• The basics of machine learning and neurons (just a review to get you warmed up!)

• Neural networks for classification and regression (just a review to get you warmed up!)

• How to model sequence data

• How to model time series data

• How to model text data for NLP (including preprocessing steps for text)

• How to build an RNN using Tensorflow 2

• How to use a GRU and LSTM in Tensorflow 2

• How to do time series forecasting with Tensorflow 2

• How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!)

• How to use Embeddings in Tensorflow 2 for NLP

• How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. I am always available to answer your questions and help you along your data science journey.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

See you in class!

"If you can't implement it, you don't understand it"

• Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

• My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

• Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

• After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

• matrix addition, multiplication

• basic probability (conditional and joint distributions)

• Python coding: if/else, loops, lists, dicts, sets

• Numpy coding: matrix and vector operations, loading a CSV file

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

• Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

Who this course is for:

• If you want to level up with deep learning, take this course.
• If you are a student or professional who wants to apply deep learning to time series or sequence data, take this course.
• If you want to learn about word embeddings and language modeling, take this course.
• If you want to improve the performance you got with Hidden Markov Models, take this course.
• If you’re interested the techniques that led to new developments in machine translation, take this course.
• If you have no idea about deep learning, don’t take this course, take the prerequisites.
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Course Details:

• 12 hours on-demand video
• 1 article
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

Deep Learning: Recurrent Neural Networks in Python udemy courses free download

GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence