Deep Learning: Recurrent Neural Networks in Python

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

4.46 (5653 reviews)

Udemy

platform

English

language

Data Science

What you will 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

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Course Gallery

Deep Learning: Recurrent Neural Networks in Python – Screenshot 1 — Screenshot 1 – Deep Learning: Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python – Screenshot 2 — Screenshot 2 – Deep Learning: Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python – Screenshot 3 — Screenshot 3 – Deep Learning: Recurrent Neural Networks in Python

Deep Learning: Recurrent Neural Networks in Python – Screenshot 4 — Screenshot 4 – Deep Learning: Recurrent Neural Networks in Python

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Comidoc Review

Our Verdict

Deep Learning: Recurrent Neural Networks in Python offers an extensive exploration into GRU, LSTM, time series forecasting, natural language processing, and image classification. With clear theoretical explanations and hands-on experience in Tensorflow 2, this course is particularly valuable for those looking to grasp the intricacies of various RNN variants. However, be prepared for an independent learning approach due to limited exercises, occasional disorganization in structure, and varying teaching intensity throughout.

What We Liked

Covers a wide range of deep learning topics including time series forecasting, natural language processing, and image classification using recurrent neural networks (RNNs)
In-depth explanations of theoretical foundations and practical applications of GRU, LSTM, and other RNN variants
High-quality code demonstrations in Tensorflow 2 with clear instruction on how to mitigate the vanishing gradient problem
Engaging teaching style with a good sense of humor, making the learning process enjoyable and effective

Potential Drawbacks

Some students may find the pace slow and lacking specific exercises or capstone projects, requiring independent initiative to reinforce understanding
Occasional disorganization in course structure, potentially causing confusion for beginners
Lack of access to promised code notebooks for certain students, impacting the learning experience negatively
Overemphasis on theory and repetition in some parts of the course may deter some learners seeking a more concise approach