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Home » Technology » AI / Machine Learning » TensorFlow Machine Learning Projects

Course Skill Level:

Foundational to Intermediate

Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    TFMLPRL21E09

Who should attend & recommended skills:

Basic Python experience

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Python skills who want to implement TensorFlow’s offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects.
  • Skill-level: Foundation-level TensorFlow Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience) required

About this course

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.

Skills acquired & topics covered

  • Using machine learning and deep learning principles to build real-world projects
  • Getting to grips with TensorFlow’s impressive range of module offerings
  • Implementing projects on GANs, reinforcement learning, and capsule network
  • Understanding the TensorFlow ecosystem using various datasets and techniques
  • Creating recommendation systems for quality product recommendations
  • Building projects using CNNs, NLP, and Bayesian neural networks
  • Playing Pac-Man using deep reinforcement learning
  • Deploying scalable TensorFlow-based machine learning systems
  • Generating your own book script using RNNs

Course breakdown / modules

  • What is TensorFlow?
  • The TensorFlow core
  • Computation graph
  • Machine learning, classification, and logistic regression
  • Logistic regression with TensorFlow
  • Logistic regression with Keras

  • 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

  • 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

  • What is TensorFlow Lite?
  • Classification Model Evaluation Metrics
  • Classifying digits using TensorFlow Lite

  • Speech-to-text frameworks and toolkits
  • Google Speech Commands Dataset
  • Neural network architecture
  • Training the model

  • 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

  • Understanding auto-encoders
  • Building a fraud detection model

  • Understanding Bayesian deep learning
  • Understanding TensorFlow probability, variational inference, and Monte Carlo methods
  • Building a Bayesian neural network

  • Understanding generative models
  • Understanding DiscoGANs
  • Building a DiscoGAN model

  • 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

  • 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

  • Introducing Apache Spark
  • Understanding distributed TensorFlow
  • Learning about TensorFlowOnSpark
  • Object detection using TensorFlowOnSpark and Sparkdl

  • Understanding recurrent neural networks
  • Pre-processing the data
  • Defining the model
  • Training the model
  • Defining and training a text-generating model
  • Generating book scripts

  • 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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