Recurrent Neural Networks (RNNs) are a powerful class of neural networks designed for sequence data, making them ideal for time series prediction and natural language processing tasks. This course begins with an introduction to the fundamental concepts of RNNs and explores their application in forecasting and time series prediction. You will delve into coding with TensorFlow, learning how to implement autoregressive models and simple RNNs for various predictive tasks.
Deep Learning - Recurrent Neural Networks with TensorFlow
Instructeur : Packt - Course Instructors
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Expérience recommandée
Ce que vous apprendrez
Identify the fundamental concepts and structures of Recurrent Neural Networks
Implement autoregressive linear models and RNNs for time series prediction in TensorFlow
Assess the performance of RNN models in real-world applications, including stock return prediction and image classification
Develop and fine-tune RNN models for complex tasks, such as text classification and long-distance sequence prediction
Compétences que vous acquerrez
- Catégorie : Machine Learning
- Catégorie : Simple RNNs
- Catégorie : TensorFlow 2
- Catégorie : Data Science
- Catégorie : Recurrent Neural Networks
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Il y a 3 modules dans ce cours
In this module, we will introduce the course by outlining the key topics and objectives. You will get an overview of what to expect and understand how each section is structured to help you achieve your learning goals. This initial module sets the stage for a successful learning journey.
Inclus
2 vidéos
In this module, we will delve into the intricacies of recurrent neural networks (RNNs) and their applications in handling sequence data and time series forecasting. You will learn to build and evaluate models for predicting future values, understand the theoretical foundations of RNNs, and explore advanced units like GRU and LSTM. Practical coding sessions will reinforce your understanding, allowing you to apply these concepts to real-world data, including stock return predictions and image classification.
Inclus
20 vidéos
In this module, we will explore the essentials of Natural Language Processing (NLP), starting with the concept of embeddings and their importance in understanding text data. You will learn to set up the necessary coding environment for NLP tasks, preprocess text data effectively, and build text classification models using Long Short-Term Memory (LSTM) networks. This module will equip you with the foundational skills needed for various NLP applications.
Inclus
4 vidéos1 devoir
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DeepLearning.AI
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Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.