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TensorFlow Machine Learning Projects

  • Course Code: Data Science - TensorFlow Machine Learning Projects
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 4 Days Audience: This course is geared for Python experienced developers, analysts or others wants to Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level TensorFlow Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others wants to Implement TensorFlow’s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this course, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the course, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this course, you’ll have gained the required expertise to build full-fledged machine learning projects at work. 

Working in a hands-on learning environment, led by our TensorFlow Machine Learning expert instructor, students will learn about and explore: 

  • Use machine learning and deep learning principles to build real-world projects 
  • Get to grips with TensorFlow’s impressive range of module offerings 
  • Implement projects on GANs, reinforcement learning, and capsule network 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand the TensorFlow ecosystem using various datasets and techniques 
  • Create recommendation systems for quality product recommendations 
  • Build projects using CNNs, NLP, and Bayesian neural networks 
  • Play Pac-Man using deep reinforcement learning 
  • Deploy scalable TensorFlow-based machine learning systems 
  • Generate your own book script using RNNs 

Audience & Pre-Requisites 

This course is for those wants to Implement TensorFlow’s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects. 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Overview of TensorFlow and Machine Learning 
  • Overview of TensorFlow and Machine Learning 
  • What is TensorFlow? 
  • The TensorFlow core 
  • Computation graph 
  • Machine learning, classification, and logistic regression 
  • Logistic regression with TensorFlow 
  • Logistic regression with Keras 
  1. Using Machine Learning to Detect Exoplanets in Outer Space 
  • Using Machine Learning to Detect Exoplanets in Outer Space 
  • What is a decision tree? 
  • Why do we need ensembles? 
  • Decision tree-based ensemble methods 
  • Decision tree-based ensembles in TensorFlow 
  • Detecting exoplanets in outer space 
  • Building a TFBT model for exoplanet detection 
  1. Sentiment Analysis in Your Browser Using TensorFlow.js 
  • Sentiment Analysis in Your Browser Using TensorFlow.js 
  • Understanding TensorFlow.js 
  • Understanding Adam Optimization 
  • Understanding categorical cross entropy loss 
  • Understanding word embeddings 
  • Building the sentiment analysis model 
  • Running the model on a browser using TensorFlow.js 
  1. Digit Classification Using TensorFlow Lite 
  • Digit Classification Using TensorFlow Lite 
  • What is TensorFlow Lite? 
  • Classification Model Evaluation Metrics 
  • Classifying digits using TensorFlow Lite 
  1. Speech to Text and Topic Extraction Using NLP 
  • Speech to Text and Topic Extraction Using NLP 
  • Speech-to-text frameworks and toolkits 
  • Google Speech Commands Dataset 
  • Neural network architecture 
  • Training the model 
  1. Predicting Stock Prices using Gaussian Process Regression 
  • Predicting Stock Prices using Gaussian Process Regression 
  • Understanding Bayes’ rule 
  •  Introducing Bayesian inference 
  • Introducing Gaussian processes 
  • Applying GPs to stock market prediction 
  • Creating a stock price prediction model 
  • Understanding the results obtained 
  1. Credit Card Fraud Detection using Autoencoders 
  • Credit Card Fraud Detection using Autoencoders 
  • Understanding auto-encoders 
  • Building a fraud detection model 
  1. Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks 
  • Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks 
  • Understanding Bayesian deep learning 
  • Understanding TensorFlow probability, variational inference, and Monte Carlo methods 
  • Building a Bayesian neural network 
  1. Generating Matching Shoe Bags from Shoe Images Using DiscoGANs 
  • Generating Matching Shoe Bags from Shoe Images Using DiscoGANs 
  • Understanding generative models 
  • Understanding DiscoGANs 
  • Building a DiscoGAN model 
  1. Classifying Clothing Images using Capsule Networks 
  • Classifying Clothing Images using Capsule Networks 
  • Understanding the importance of capsule networks 
  • Understanding capsules 
  • The dynamic routing algorithm 
  • CapsNet for classifying Fashion MNIST images 
  • Training and testing the model 
  • Reconstructing sample images 
  • Limitations of capsule networks 
  1. Making Quality Product Recommendations Using TensorFlow 
  • Making Quality Product Recommendations Using TensorFlow 
  • Recommendation systems 
  • Content-based filtering 
  • Collaborative filtering 
  • Hybrid systems 
  • Matrix factorization 
  • Introducing the Retailrocket dataset 
  • Exploring the Retailrocket dataset 
  • Pre-processing the data 
  • The matrix factorization model for Retailrocket recommendations 
  • The neural network model for Retailrocket recommendations 
  1. Object Detection at a Large Scale with TensorFlow 
  • Object Detection at a Large Scale with TensorFlow 
  • Introducing Apache Spark 
  • Understanding distributed TensorFlow 
  • Learning about TensorFlowOnSpark 
  • Object detection using TensorFlowOnSpark and Sparkdl 
  1. Generating Book Scripts Using LSTMs 
  • Generating Book Scripts Using LSTMs 
  • Understanding recurrent neural networks 
  • Pre-processing the data 
  • Defining the model 
  • Training the model 
  • Defining and training a text-generating model 
  • Generating book scripts 
  1. Playing Pacman Using Deep Reinforcement Learning 
  • Playing Pacman Using Deep Reinforcement Learning 
  • Reinforcement learning 
  • Reinforcement learning versus supervised and unsupervised learning 
  • Components of Reinforcement Learning 
  • OpenAI Gym  
  • Creating a Pacman game in OpenAI Gym  
  • DQN for deep reinforcement learning 
  • Applying DQN to a game 
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