Deep Learning With Apache Spark Solutions

Implement practical hands-on examples with over 55 recipes that streamline Deep Learning with Apache Spark

What you'll 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 ee using Keras.
Manipulate and merge the MovieLens datasets.

Requirements
Basic understanding of Machine Learning and Big Data concepts

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 b 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 ee 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.

Overview
Section 1: Apache Spark Deep Learning Recipes

Lecture 1 The Course overview

Lecture 2 Creating a Dataframes in Pyspark

Lecture 3 Manipulating Columns in a Pyspark Dataframes

Lecture 4 Converting a PySparkdataframe to an array

Lecture 5 Visualizing an Array in a Scatterplot

Lecture 6 Setting up Weights and Biases for Input into the Neural Network

Lecture 7 Normalizing the Input Data for the Neural Network

Lecture 8 Validating Array for Optimal Neural Network Performance

Lecture 9 Setting up the Activation Function with Sigmoid

Lecture 10 Creating the Sigmoid Derivative Function

Lecture 11 Calculating the Cost Function in a Neural Network

Lecture 12 Predicting Gender based on Height and Weight

Lecture 13 Visualizing Prediction Scores

Lecture 14 Pain Point #1: Importing MNIST Images

Lecture 15 Pain Point #2: Visualizing MNIST Images

Lecture 16 Pain Point #3: Exporting MNIST Images as Files

Lecture 17 Pain Point #4: Augmenting MNIST Images

Lecture 18 Pain Point #5: Utilizing Alternate Sources for Trained Images

Lecture 19 Pain Point #6: Prioritizing High-Level Libraries for CNNs

Lecture 20 ing the San Francisco Fire Department Calls Dataset

Lecture 21 Identifying the Target Variable of the Logistic Regression Model

Lecture 22 Preparing Feature Variables for the Logistic Regression Model

Lecture 23 Applying the Logistic Regression Model

Lecture 24 Evaluating the Accuracy of the Logistic Regression Model

Lecture 25 ing and Analyzing the Therapy Bot Session Dataset

Lecture 26 Visualizing Word Counts in the Dataset

Lecture 27 Calculating Sennt Analysis of Text

Lecture 28 Removing Stop Words from the Text

Lecture 29 Training and Evaluating TF-IDF Model Performance

Lecture 30 Comparing Model Performance to a Baseline Score

Lecture 31 ing Stock Market Data for Apple

Lecture 32 Exploring and Visualizing Stock Market Data for Apple

Lecture 33 Preparing Stock Data for Model Performance

Lecture 34 Building the LSTM Model

Lecture 35 Evaluating the Model

Section 2: Apache Spark Deep Learning Advanced Recipes

Lecture 36 The Course overview

Lecture 37 ing Novels/Books that will be used as Input Text

Lecture 38 Preparing and Cleansing Data

Lecture 39 Tokenizing Sentences

Lecture 40 Generating Similar Text using the Model

Lecture 41 ing the King County House Sales Dataset

Lecture 42 Perfog Exploratory Analysis and Visualization

Lecture 43 Plotting Correlation Between Price and Other Features

Lecture 44 Predicting the Price of a House

Lecture 45 ing and Loading the MIT-CBCL Dataset into the Memory

Lecture 46 Plotting and Visualizing Images from the Directory

Lecture 47 Preprocessing Images

Lecture 48 Acquiring Data

Lecture 49 Importing the Necessary Libraries

Lecture 50 Preparing the Data

Lecture 51 Building and Training the Model

Lecture 52 Visualizing Further

Lecture 53 Analyzing Further

Lecture 54 ing MovieLens Datasets

Lecture 55 Manipulating and Meg the MovieLens Datasets

Lecture 56 Exploring the MovieLens Datasets

Lecture 57 Preparing Dataset for the Deep Learning Pipeline

Lecture 58 Applying the Deep Learning Model with Keras

Lecture 59 Evaluating the recommendation ee's accuracy

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.

HomePage:

https://www.udemy.com/course/learning-path-deep-learning-with-apache-spark-solutions/

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