Mastering Deep Learning using Apache Spark

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Develop industrial solutions based on deep learning models with Apache SparkDeep learning has solved tons of interesting real-world problems in recent years. Apache Spark has emerged as the most important and promising machine learning tool and currently a stronger challenger of the Hadoop ecosystem. In this course, you’ll learn about the major branches of AI and get familiar with several core models of Deep Learning in its natural way.You’ll begin with building deep learning networks to deal with speech data and explore tricks to solve NLP problems and classify video frames using RNN and LSTMs. You’ll also learn to implement the anomaly detection model that leverages reinforcement learning techniques to improve cyber security.Moving on, you’ll explore some more advanced topics by performing prediction classification on image data using the GAN encoder and decoder. Then you’ll configure Spark to use multiple workers and CPUs to distribute your Neural Network training. Finally, you’ll track progress, solve the most common problems in your neural network, and debug your models that run within the distributed Spark engine.About the AuthorTomasz LelekTomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He has worked with Spark API and the ML API for the past five years and has production experience in processing petabytes of data.He is passionate about nearly everything associated with software development and believes that we should always try to consider different solutions and approaches before solving a problem. He was a speaker at conferences in Poland, Confitura and JDD (Java Developers Day), and the Krakow Scala User Group. He has also conducted a live coding session at Geecon Conference.

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About this course

Configure a Convolutional Neural Network (CNN) to extract value from images
Create a deep network with multiple layers to perform computer vision
Classify speech and audio data
Leverage RNN and LSTMs for video classification for hospital data
Improve cybersecurity with deep reinforcement learning
Use a generative adversarial network for training
Create highly distributed algorithms using Spark

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This centre's achievements

2021

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The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 6 years

Subjects

  • Ms Word
  • Network Training
  • Finance
  • Network
  • Apache
  • Networks
  • Java
  • NLP
  • Word

Course programme

Convolutional Neural Networks for Speech Recognition (NLP) 5 lectures 17:46 The Course Overview This video will give you an overview about the course. Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Convolutional Neural Networks for Speech Recognition (NLP) - Quiz Convolutional Neural Networks for Speech Recognition (NLP) 5 lectures 17:46 The Course Overview This video will give you an overview about the course. Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Convolutional Neural Networks for Speech Recognition (NLP) - Quiz The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. The Course Overview This video will give you an overview about the course. This video will give you an overview about the course. This video will give you an overview about the course. Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Analyzing Input Text Data That Will Need to Be Classified In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
In this video, we will analyze input text data to be classified.
  • Delve into business domain speech data
  • Analyze texts from finance, health, and science
  • Load data into ML model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Configuring Word Vectors That Will Be Used in Our Network In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
In this video, we will configure word vectors that will be used in our network.
  • Load data into paragraph vectors API construct
  • Set the tokenizer
  • Create the model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Adding Layers to Deep Neural Network In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
In this video, we will add layers to deep neural network.
  • Build classification model
  • Leverage labelled data
  • Load unlabeled data will be used to validate model
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Asserting Classification of Input Sentences In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
In this video, we will assert classification of input sentences.
  • Transform unlabeled data into feature vector
  • Assign document into classes
  • Validate results
Convolutional Neural Networks for Speech Recognition (NLP) - Quiz Convolutional Neural Networks for Speech Recognition (NLP) - Quiz Convolutional Neural Networks for Speech Recognition (NLP) - Quiz Convolutional Neural Networks for Speech Recognition (NLP) - Quiz Performing Video Classification Using RNN and LSTMs 4 lectures 18:41 Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
Adding RNN and LSTMs to Network to Perform a Task Better In this video, we will add RNN and LSTM to network to perform a task better.
  • Validate neural network parameters
  • Configure LSTM layer
  • Start training
Testing and Validating Deep Learning Model In this video, we will test and validate our deep learning model.
  • Write code for cross-validation
  • Start code
  • Validate the video frames which are assigned to proper classes
Performing Video Classification Using RNN and LSTMs - Quiz Performing Video Classification Using RNN and LSTMs. 4 lectures 18:41 Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
Adding RNN and LSTMs to Network to Perform a Task Better In this video, we will add RNN and LSTM to network to perform a task better.
  • Validate neural network parameters
  • Configure LSTM layer
  • Start training
Testing and Validating Deep Learning Model In this video, we will test and validate our deep learning model.
  • Write code for cross-validation
  • Start code
  • Validate the video frames which are assigned to proper classes
Performing Video Classification Using RNN and LSTMs - Quiz Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Generating Input Video Data In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
In this video, we will generate input video data.
  • Generate input video
  • Create MP4 files for different kinds of shapes
  • Add text labels per frame
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
Creating a Neural Network for Video Classification In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
In this video, we will create a neural network for video classification.
  • Configure multi-layer
  • Adapt to labelled input data
  • Create last layer that produces proper number of classes
In this video, we will create a neural network for video classification In this video, we will learn about the...

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Basics of Machine Learning

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Mastering Deep Learning using Apache Spark

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