Deep Learning: Recurrent Neural NetWorks in Python 2021

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Deep Learning: Recurrent Neural Networks in Python 2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 3.66 GB | Duration: 11h 54m​

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

What you'll learn
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

Requirements
Basic math (taking derivatives, matrix arithmetic, probability) is helpful
Python, Numpy, Matplotlib

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:
Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP
Software Engineers and Data Scientists who want to level up their career

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2.85 GB | 26min 33s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Introduction and Outline.mp4 (6.53 MB)
2 Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 (13.19 MB)
3 Where to get the code.mp4 (26.87 MB)
4 How to Succeed in this Course.mp4 (16.24 MB)
1 Intro to Google Colab, how to use a GPU or TPU for free.mp4 (43.38 MB)
2 Uploading your own data to Google Colab.mp4 (62.76 MB)
3 Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 (55.57 MB)
4 Temporary 403 Errors.mp4 (20.81 MB)
1 Review Section Introduction.mp4 (5.02 MB)
10 Saving and Loading a Model.mp4 (25.15 MB)
11 Suggestion Box.mp4 (23.29 MB)
2 What is Machine Learning.mp4 (34.45 MB)
3 Code Preparation (Classification Theory).mp4 (28.31 MB)
4 Classification Notebook.mp4 (111.07 MB)
5 Code Preparation (Regression Theory).mp4 (12.69 MB)
6 Regression Notebook.mp4 (148.95 MB)
7 The Neuron.mp4 (45.43 MB)
8 How does a model learn.mp4 (37.69 MB)
9 Making Predictions.mp4 (16.69 MB)
1 Artificial Neural Networks Section Introduction.mp4 (11.85 MB)
10 ANN for Regression.mp4 (60.75 MB)
2 Forward Propagation.mp4 (19.16 MB)
3 The Geometrical Picture.mp4 (20.83 MB)
4 Activation Functions.mp4 (39.18 MB)
5 Multiclass Classification.mp4 (20.4 MB)
6 How to Represent Images.mp4 (26.94 MB)
7 Color Mixing Clarification.mp4 (1.86 MB)
8 Code Preparation (ANN).mp4 (22.25 MB)
9 ANN for Image Classification.mp4 (38.36 MB)
1 Sequence Data.mp4 (42.69 MB)
10 GRU and LSTM (pt 2).mp4 (21.84 MB)
11 A More Challenging Sequence.mp4 (56.71 MB)
12 Demo of the Long Distance Problem.mp4 (91.05 MB)
13 RNN for Image Classification (Theory).mp4 (11.58 MB)
14 RNN for Image Classification (Code).mp4 (20.34 MB)
15 Stock Return Predictions using LSTMs (pt 1).mp4 (39.02 MB)
16 Stock Return Predictions using LSTMs (pt 2).mp4 (18.04 MB)
17 Stock Return Predictions using LSTMs (pt 3).mp4 (62.38 MB)
18 Other Ways to Forecast.mp4 (11.49 MB)
2 Forecasting.mp4 (19.76 MB)
3 Autoregressive Linear Model for Time Series Prediction.mp4 (63.6 MB)
4 Proof that the Linear Model Works.mp4 (6.94 MB)
5 Recurrent Neural Networks.mp4 (35.54 MB)
6 RNN Code Preparation.mp4 (8.38 MB)
7 RNN for Time Series Prediction.mp4 (56.19 MB)
8 Paying Attention to Shapes.mp4 (48.61 MB)
9 GRU and LSTM (pt 1).mp4 (41.84 MB)
1 Embeddings.mp4 (22.39 MB)
2 Code Preparation (NLP).mp4 (33.05 MB)
3 Text Preprocessing.mp4 (87.68 MB)
4 Text Classification with LSTMs.mp4 (184.65 MB)
1 Mean Squared Error.mp4 (16.77 MB)
2 Binary Cross Entropy.mp4 (9.8 MB)
3 Categorical Cross Entropy.mp4 (13.55 MB)
1 Gradient Descent.mp4 (14.01 MB)
2 Stochastic Gradient Descent.mp4 (11.63 MB)
3 Momentum.mp4 (16.2 MB)
4 Variable and Adaptive Learning Rates.mp4 (17.04 MB)
5 Adam (pt 1).mp4 (55.14 MB)
6 Adam (pt 2).mp4 (52.78 MB)
1 Pre-Installation Check.mp4 (8.92 MB)
2 Anaconda Environment Setup.mp4 (167.89 MB)
3 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 (109.22 MB)
1 Beginner's Coding Tips.mp4 (27.98 MB)
2 How to Code by Yourself (part 1).mp4 (56.13 MB)
3 How to Code by Yourself (part 2).mp4 (20.87 MB)
4 Proof that using Jupyter Notebook is the same as not using it.mp4 (64.31 MB)
5 Python 2 vs Python 3.mp4 (7.61 MB)
6 How to use Github & Extra Coding Tips (Optional).mp4 (29.65 MB)
1 How to Succeed in this Course (Long Version).mp4 (17.87 MB)
2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 (42.45 MB)
3 Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 (75.68 MB)
4 Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (81.22 MB)
1 What is the Appendix.mp4 (6.14 MB)
2 BONUS.mp4 (19.55 MB)

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