What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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Instructor: Packt - Course Instructors
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Recommended experience
Intermediate level
Course for deep learning enthusiasts to implement RNNs in TensorFlow 2. Requires Python skills, ANN building, and experience with NumPy/Matplotlib.
Recommended experience
Intermediate level
Course for deep learning enthusiasts to implement RNNs in TensorFlow 2. Requires Python skills, ANN building, and experience with NumPy/Matplotlib.
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
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September 2024
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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.
As the course progresses, you will encounter more sophisticated RNN architectures such as GRUs and LSTMs. These units are essential for handling complex sequences and long-distance dependencies in data. Practical sessions will guide you through using these models for challenging tasks, including stock return prediction and image classification on the MNIST dataset. The course also covers the critical aspect of managing data shapes and ensuring your models are well-structured and efficient. Towards the end, the course shifts focus to natural language processing (NLP), where you will explore embeddings, text preprocessing, and text classification using LSTMs. By combining theoretical knowledge with hands-on coding exercises, you will develop a robust understanding of how to leverage RNNs for various applications. Whether you are predicting stock prices or classifying text, this course equips you with the skills needed to succeed in the field of deep learning. This course is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to learn and implement recurrent neural networks for time series analysis and natural language processing. Basic knowledge of Python and TensorFlow is recommended.
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.
2 videos
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.
20 videos
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.
4 videos1 assignment
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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DeepLearning.AI
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Google Cloud
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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.
If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.
This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.
You will be eligible for a full refund until two weeks after your payment date, or (for courses that have just launched) until two weeks after the first session of the course begins, whichever is later. You cannot receive a refund once you’ve earned a Course Certificate, even if you complete the course within the two-week refund period. See our full refund policy.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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