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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.

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Unlimited Duration

Last Updated

February 4, 2021

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Certification

Course Description

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.

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Course Curriculum

    • Overview of TensorFlow and Machine Learning 00:00:00
    • What is TensorFlow? 00:00:00
    • The TensorFlow core 00:00:00
    • Computation graph 00:00:00
    • Machine learning, classification, and logistic regression 00:00:00
    • Logistic regression with TensorFlow 00:00:00
    • Logistic regression with Keras 00:00:00
    • Using Machine Learning to Detect Exoplanets in Outer Space 00:00:00
    • What is a decision tree? 00:00:00
    • Why do we need ensembles? 00:00:00
    • Decision tree-based ensemble methods 00:00:00
    • Decision tree-based ensembles in TensorFlow 00:00:00
    • Detecting exoplanets in outer space 00:00:00
    • Building a TFBT model for exoplanet detection 00:00:00
    • Sentiment Analysis in Your Browser Using TensorFlow.js 00:00:00
    • Understanding TensorFlow.js 00:00:00
    • Understanding Adam Optimization 00:00:00
    • Understanding categorical cross entropy loss 00:00:00
    • Understanding word embeddings 00:00:00
    • Building the sentiment analysis model 00:00:00
    • Running the model on a browser using TensorFlow.js 00:00:00
    • Digit Classification Using TensorFlow Lite 00:00:00
    • What is TensorFlow Lite? 00:00:00
    • Classification Model Evaluation Metrics 00:00:00
    • Classifying digits using TensorFlow Lite 00:00:00
    • Speech to Text and Topic Extraction Using NLP 00:00:00
    • Speech-to-text frameworks and toolkits 00:00:00
    • Google Speech Commands Dataset 00:00:00
    • Neural network architecture 00:00:00
    • Training the model 00:00:00
    • Predicting Stock Prices using Gaussian Process Regression 00:00:00
    • Understanding Bayes’ rule 00:00:00
    • Introducing Bayesian inference 00:00:00
    • Introducing Gaussian processes 00:00:00
    • Applying GPs to stock market prediction 00:00:00
    • Creating a stock price prediction model 00:00:00
    • Understanding the results obtained 00:00:00
    • Credit Card Fraud Detection using Autoencoders 00:00:00
    • Understanding auto-encoders 00:00:00
    • Building a fraud detection model 00:00:00
    • Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks 00:00:00
    • Understanding Bayesian deep learning 00:00:00
    • Understanding TensorFlow probability, variational inference, and Monte Carlo methods 00:00:00
    • Building a Bayesian neural network 00:00:00
    • Generating Matching Shoe Bags from Shoe Images Using DiscoGANs 00:00:00
    • Understanding generative models 00:00:00
    • Understanding DiscoGANs 00:00:00
    • Building a DiscoGAN model 00:00:00
    • Classifying Clothing Images using Capsule Networks 00:00:00
    • Understanding the importance of capsule networks 00:00:00
    • Understanding capsules 00:00:00
    • The dynamic routing algorithm 00:00:00
    • CapsNet for classifying Fashion MNIST images 00:00:00
    • Training and testing the model 00:00:00
    • Reconstructing sample images 00:00:00
    • Limitations of capsule networks 00:00:00
    • Making Quality Product Recommendations Using TensorFlow 00:00:00
    • Recommendation systems 00:00:00
    • Content-based filtering 00:00:00
    • Collaborative filtering 00:00:00
    • Hybrid systems 00:00:00
    • Matrix factorization 00:00:00
    • Introducing the Retailrocket dataset 00:00:00
    • Exploring the Retailrocket dataset 00:00:00
    • Pre-processing the data 00:00:00
    • The matrix factorization model for Retailrocket recommendations 00:00:00
    • The neural network model for Retailrocket recommendations 00:00:00
    • Object Detection at a Large Scale with TensorFlow 00:00:00
    • Introducing Apache Spark 00:00:00
    • Understanding distributed TensorFlow 00:00:00
    • Learning about TensorFlowOnSpark 00:00:00
    • Object detection using TensorFlowOnSpark and Sparkdl 00:00:00
    • Generating Book Scripts Using LSTMs 00:00:00
    • Understanding recurrent neural networks 00:00:00
    • Pre-processing the data 00:00:00
    • Defining the model 00:00:00
    • Training the model 00:00:00
    • Defining and training a text-generating model 00:00:00
    • Generating book scripts 00:00:00
    • Playing Pacman Using Deep Reinforcement Learning 00:00:00
    • Reinforcement learning 00:00:00
    • Reinforcement learning versus supervised and unsupervised learning 00:00:00
    • Components of Reinforcement Learning 00:00:00
    • OpenAI Gym 00:00:00
    • Creating a Pacman game in OpenAI Gym 00:00:00
    • DQN for deep reinforcement learning 00:00:00
    • Applying DQN to a game 00:00:00

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