Last updated 1/2019MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.38 GB | Duration: 3h 20m
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. 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