Deep Learning: Recurrent Neural Networks with Python

Packt via Coursera Specialization

Go to class Write review

Details

Go to class

Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

With the exponential growth of user-generated data, mastering RNNs is essential for deep learning engineers to perform tasks like classification and prediction. Architectures such as RNNs, GRUs, and LSTMs are top choices, making mastering RNNs a priority. This course starts with the basics and gradually builds your theoretical and practical skills to build, train, and implement RNNs. You will engage in several exercises on topics like gradient descents in RNNs, GRUs, and LSTMs, and learn to implement RNNs using TensorFlow. The course concludes with two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end, you will be equipped to confidently use and implement RNNs in your projects. No prior RNN knowledge is required; Python experience is helpful. This course is ideal for beginners, seasoned data scientists looking to start with RNNs, business analysts, and those wanting to implement RNNs in projects. Through engaging exercises, carefully designed modules, and realistic RNN implementations, you will master RNNs, gain an overview of deep neural networks, understand RNN architectures, and perform text classification using TensorFlow.

Syllabus

Course 1: Introduction to RNN and DNN
Course 2: RNN Architecture and Sentiment Classification
Course 3: Advanced RNN Concepts and Projects

Courses

0 reviews

View details

This advanced course on Recurrent Neural Networks (RNNs) addresses key challenges like the vanishing gradient problem and provides solutions such as Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks. You'll start with an overview of improved RNN modules and delve into bidirectional RNNs and attention models, establishing a strong foundation in advanced RNN concepts. Practical implementation using TensorFlow is emphasized, with projects like text generation and stock price prediction to solidify your learning. This course ensures you gain the skills necessary to tackle real-world AI problems confidently. Through video tutorials, real-world projects, and hands-on exercises, you'll acquire the advanced knowledge and skills needed to excel in AI. By the end, you'll develop and apply advanced RNN models, understand and implement GRUs, LSTMs, and attention mechanisms, utilize TensorFlow for RNN models, and apply these models to projects like text generation and stock price prediction. Designed for data scientists, machine learning engineers, and AI enthusiasts with a solid understanding of basic RNNs and neural networks, the course combines in-depth theoretical lessons with extensive practical applications.
0 reviews

View details

Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN architectures, and utilize PyTorch for model building and optimization. By the end, you'll have a robust knowledge of RNNs and DNNs and the confidence to apply these techniques in real-world scenarios. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming (preferably Python) and statistics knowledge, this course combines theory with practical application through video lectures, hands-on exercises, and real-world examples.
0 reviews

View details

Artificial Intelligence is revolutionizing data analysis. This course delves into Recurrent Neural Networks (RNNs), starting with basic memory models and advancing to deep RNN structures. You'll explore RNN models like ManyToMany, ManyToOne, and OneToMany through practical exercises, culminating in sentiment classification for sophisticated text analysis and prediction. You will gain a solid grasp of RNN architectures and implement sentiment classification models. Key features include detailed RNN architecture, practical implementation using PyTorch, sentiment classification applications, and hands-on exercises. By the end, you'll develop and apply various RNN models for tasks like sentiment analysis and language modeling, understand fixed-length and infinite memory models, utilize PyTorch for building and optimizing RNN models, and perform advanced tasks like gradient descent and backpropagation through time. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming and neural network knowledge, the course combines theory with hands-on application via video tutorials and real-world examples.