Deep Learning with Apache Spark Solutions
Implement practical hands-on examples with over 55 recipes that streamline Deep Learning with Apache Spark
Description
With Deep Learning gaining rapid mainstream adoption in modern-day industries, organizations are looking for ways to unite popular big data tools with highly efficient Deep Learning libraries: TensorFlow and Keras which focuses on the pain points of Convolution Neural Networks. As a result, you'll have the expertise to train and deploy efficient Deep Learning models on Apache Spark.
Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.
This Course is a fast-paced guide to implementing practical hands-on examples, streamlining Deep Learning with Apache Spark. You’ll begin with understanding practical Machine Learning and Deep Learning concepts to apply built-in Machine Learning libraries within Spark. Explore libraries that are compatible with TensorFlow and Keras. You’ll create and visualize word vectors using Word2vec, also create a movie recommendation engine using Keras. Finally, you’ll implement practical hands-on examples streamlining Deep Learning with Apache Spark Solutions.
By the end of this course, you'll implement practical hands-on examples with over 55 recipes that streamline Deep Learning with Apache Spark.
What You Will Learn!
- Understand practical machine learning and deep learning concepts.
- Apply built-in Machine Learning libraries within Spark.
- Explore libraries that are compatible with TensorFlow and Keras.
- Explore NLP models such as Word2vec and TF-IDF on Spark.
- Face recognition using Deep Convolutional Networks.
- Create and visualize word vectors using Word2vec.
- Create a movie recommendation engine using Keras.
- Manipulate and merge the MovieLens datasets.
Who Should Attend!
- This course is perfect for: Data Scientist, Data Analysts, Big Data Architects, Anyone with a basic understanding of Machine Learning and Big Data concepts interested in implementing practical hands-on examples, streamlining Deep Learning with Apache Spark.