Курс Deep Learning: Recurrent Neural Networks in Python

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11.5 часов курса
О курсе

 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!

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 "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

Программа
Introduction and Outline
Outline of this Course
Review of Important Deep Learning Concepts
Where to get the Code and Data
How to Succeed in this Course
The Simple Recurrent Unit
Architecture of a Recurrent Unit
Prediction and Relationship to Markov Models
Unfolding a Recurrent Network
Backpropagation Through Time (BPTT)
We discuss how to do gradient descent when time is involved, pitfalls like the vanishing gradient problem and exploding gradient problem, the gradient clipping technique, and truncated backpropagation through time.
The Parity Problem - XOR on Steroids
The Parity Problem in Code using a Feedforward ANN
Требования
  • Basic math (taking derivatives, matrix arithmetic, probability) is helpful
  • Python, Numpy, Matplotlib
Что Вы изучите?
  • Apply RNNs to Time Series Forecasting (tackle the ubiquitous "Stock Prediction" problem)
  • Apply RNNs to Natural Language Processing (NLP) and Text Classification (Spam Detection)
  • Apply RNNs to Image Classification
  • Understand the simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit)
  • Write various recurrent networks in Tensorflow 2
  • Understand how to mitigate the vanishing gradient problem
Лекторы
Lazy Programmer Inc.
Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Платформа
Udemy
Курсы Udemy подойдут для профессионального развития. Платформа устроена таким образом, что эксперты сами запускают курсы. Все материалы передаются в пожизненный доступ. На этой платформе можно найти курс, без преувеличений, на любую тему – начиная от тьюториала по какой-то камере и заканчивая теоретическим курсом по управлению финансовыми рисками. Язык и формат обучения устанавливается преподавателем, поэтому стоит внимательно изучить информацию о курсе перед покупкой.
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