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TensorFlow Intermediate
Learn how to build advanced machine-learning models using TensorFlowPreview the first lesson free — get full access to all 27 lessons.
Intermediate Provider : Josh Turner 7 chapters · 27 Lessons · 2h 20m in English
Course Description
In this course on TensorFlow Intermediate, you will learn how to build advanced machine-learning models using TensorFlow. We will explore sequences and time series forecasting, recurrent neural networks, natural language processing, and recommender systems. We’ll also begin an intro to TensorFlow Lite. By the end of this course, you will be able to confidently create and deploy machine-learning models using TensorFlow for a variety of applications.
In looking at natural language processing (NLP) and its importance in various applications, you’ll learn about text preprocessing, tokenization, and building text classification models using TensorFlow. You will also discover advanced NLP techniques like word embeddings, attention mechanisms, and transformer models.
Recommender systems will be another key focus area in this course. You will explore collaborative filtering and content-based filtering techniques, and learn how to preprocess data and build recommender models using TensorFlow. You’ll also be introduced to hybrid recommender systems that combine multiple approaches.
We’ll introduce you to TensorFlow Lite, a framework for deploying machine-learning models on mobile and edge devices. You will learn how to convert TensorFlow models to TensorFlow Lite format, deploy them on mobile and edge devices, optimize the models, and explore special use cases of TensorFlow Lite. By the end of this course, you will have a comprehensive understanding of advanced machine-learning concepts, be proficient in using TensorFlow for building different types of models, and be equipped with the skills to deploy models in real-world scenarios.
What You'll Learn
- Build sequential models for time series forecasting, including data preprocessing, training, and evaluation
- Apply recurrent neural networks (RNNs) and their variants while handling long sequences and overfitting
- Develop natural language processing models using text preprocessing, tokenization, text classification, and language generation
- Explore advanced NLP techniques including word embeddings, attention mechanisms, and transformer models
- Construct recommender systems using collaborative filtering, content-based filtering, and hybrid approaches
- Deploy machine-learning models with TensorFlow Lite, including model conversion and optimization for mobile and edge devices
Key Takeaways
- The course covers building advanced machine-learning models with TensorFlow, spanning sequences, time series forecasting, RNNs, NLP, recommender systems, and TensorFlow Lite.
- Natural language processing topics include text preprocessing, tokenization, building text classification models, and advanced techniques such as word embeddings, attention mechanisms, and transformer models.
- Recommender system content addresses collaborative filtering, content-based filtering, and hybrid recommender systems that combine multiple approaches.
- TensorFlow Lite is presented as a framework for deploying machine-learning models on mobile and edge devices, including converting models, optimizing them, and exploring special use cases.
- By the end of the course, learners are intended to confidently create and deploy machine-learning models using TensorFlow for a variety of applications.
Frequently Asked Questions
What topics does this TensorFlow Intermediate course cover?
The course explores sequences and time series forecasting, recurrent neural networks, natural language processing, recommender systems, and an introduction to TensorFlow Lite for deploying models on mobile and edge devices.
What natural language processing skills will I gain?
You will learn text preprocessing, tokenization, and building text classification models with TensorFlow, plus advanced NLP techniques such as word embeddings, attention mechanisms, and transformer models.
What types of recommender systems are taught?
The course covers collaborative filtering and content-based filtering techniques, how to preprocess data and build recommender models using TensorFlow, and hybrid recommender systems that combine multiple approaches.
What will I be able to do with TensorFlow Lite after this course?
You will learn how to convert TensorFlow models to TensorFlow Lite format, deploy them on mobile and edge devices, optimize the models, and explore special use cases of TensorFlow Lite.
What is the overall outcome of completing this course?
By the end, you will have a comprehensive understanding of advanced machine-learning concepts, be proficient in using TensorFlow for building different types of models, and be equipped with the skills to deploy models in real-world scenarios.
