- Introduction and Outline
- Where to get the Code
- How to Succeed in this Course
Online
₹ 455 3,499
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Deep Learning: Recurrent Neural Networks in Python online certification is a compelling course presented by an inspiring instructor. The cases are well-chosen, demonstrating foundations through fun, creative projects rather than lecturing on abstract concepts. The course is created by Lazy Programmer Inc. - Artificial intelligence and machine learning engineer and presented by Udemy, an ed-tech organization based in the United States that supports students with the greatest and most up-to-date skills through online courses in more than 180 countries.
As the Deep Learning: Recurrent Neural Networks in Python online training involves complex concepts, candidates should have prior knowledge of the following topics to get the most out of the course: matrix addition, multiplication, basic probability, python coding, Numpy coding, matrix, and vector operations, loading a CSV file. The course also provides 12 hours of prerecorded English lectures and an article for understanding the topics at their own pace.
The highlights
- Certificate of completion
- Self-paced course
- English videos with multi-language subtitles
- 12 hours of pre-recorded video content
- Online course
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- Pre-recorded video content
- 1 article
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Deep Learning: Recurrent Neural Networks in Python certification course, learners will about the fundamentals of machine learning and neural networks, classification, and regression using neural networks, modelling sequence data, time-series data, and modelling text data for NLP to create an RNN using TensorFlow 2. Candidates will learn to create text classification RNN and use embeddings in TensorFlow 2 for natural language processing, use TensorFlow 2 to forecast time series and predict stock prices and returns using LSTMs 2, use a GRU and an LSTM 2
Who it is for
The syllabus
Welcome
Google Colab
- Intro to Google Colab, how to use a GPU or TPU for free
- Uploading your own data to Google Colab
- Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
Machine Learning and Neurons
- Review Section Introduction
- What is Machine Learning?
- Code Preparation (Classification Theory)
- Classification Notebook
- Code Preparation (Regression Theory)
- Regression Notebook
- The Neuron
- How does a model "learn"?
- Making Predictions
- Saving and Loading a Model
- Suggestion Box
Feedforward Artificial Neural Networks
- Artificial Neural Networks Section Introduction
- Forward Propagation
- The Geometrical Picture
- Activation Functions
- Multiclass Classification
- How to Represent Images
- Code Preparation (ANN)
- ANN for Image Classification
- ANN for Regression
Recurrent Neural Networks, Time Series, and Sequence Data
- Sequence Data
- Forecasting
- Autoregressive Linear Model for Time Series Prediction
- Proof that the Linear Model Works
- Recurrent Neural Networks
- RNN Code Preparation
- RNN for Time Series Prediction
- Paying Attention to Shapes
- GRU and LSTM (pt 1)
- GRU and LSTM (pt 2)
- A More Challenging Sequence
- Demo of the Long Distance Problem
- RNN for Image Classification (Theory)
- RNN for Image Classification (Code)
- Stock Return Predictions using LSTMs (pt 1)
- Stock Return Predictions using LSTMs (pt 2)
- Stock Return Predictions using LSTMs (pt 3)
- Other Ways to Forecast
Natural language Processing (NLP)
- Embeddings
- Code Preparation (NLP)
- Text Preprocessing
- Text Classification with LSTMs
In-Depth: Loss Functions
- Mean Squared Error
- Binary Cross Entropy
- Categorical Cross Entropy
In-Depth: Gradient Descent
- Gradient Descent
- Stochastic Gradient Descent
- Momentum
- Variable and Adaptive Learning Rates
- Adam (pt 1)
- Adam (pt 2)
Extras
- Colab Notebooks
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- Beginner's Coding Tips
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS