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Practical Guide to TensorFlow for AI and Machine Learning

Practical Guide to TensorFlow for AI and Machine Learning

30 reviews between Nov 11, 2020 and Nov 01, 2023.

A practical and focused guide on using TensorFlow for AI and machine learning, written by Laurence Moroney. The book covers essential concepts and provides clear explanations and examples. It is suitable for both beginners and experienced coders. However, some readers find the Python code poorly written and the book lacking in explanations for certain topics. Overall, it is a valuable resource for learning TensorFlow.

AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence (Audible Audio
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30 reviews between Nov 11, 2020 and Nov 01, 2023.

Nov 11, 2020 - Nov 01, 2023

Featured Content

  • missing pages

    My book is missing pages 1-15.

    Sentiment
    • Negative
    Content
    • Incomplete Coverage
    Nov 01, 2023
  • Great summary of machine learning and what to do with it

    Met and exceeded my expectations. Great walkthroughs and explanations on coding machine learning into different environment (using TensorFlow). Also good starter discussion on ML techniques. I especially liked the mx+b example of neural networks, very intuitive. Answered lots of questions I had about using my models, such as with JavaScript or using transfer learning.

    The last chapter on ethics / federated learning felt a little short (but the fact it was there was already good). I would have liked a little more on dealing with / identifying model drift and training on new data while not overwriting the weights of the old. Essentially more lifecycle stuff. Discussion of ML model deployment was all TensorFlow but like I really enjoyed it all.

    Sentiment
    • Positive
    Content
    • Practical and Applicable
    Feb 05, 2022
  • good starting point for deep learning education

    Likes:
    - It is a good intro for student of AI and machine learning.
    - It helps you get a grasp on machine learning quickly. If you want to learn by doing, this is the right book for you.

    Dislikes
    - The author does not always hold you hand through the whole journety. You have to figure out certain things for yourself by looking at the example code which is available online. As the title suggests, the book is for coders who know how to read code and put together pieces of code snippets to make the examples work (but don't worry, it's not that difficult).
    - The author does not spend a whole lot of time on explaining the details of fundamental AI/ML concepts. Again, if your background is not in engineering/math/programming, you may find it hard to keep up. You might want to learn the basic concepts elsewhere first.

    All in all, this is a good intro book on AI and machine learning, but you will most likely need supplemental material to have better understanding of what's in the book. I personally prefer the teaching style of pyimagesearch.com and it university course which is more hands on and takes time explaining the difficult concepts.

    Sentiment
    • Positive
    Content
    • Incomplete Coverage
    Dec 17, 2021
  • Fantastic book for programmers

    L Moroney delivered a great book introducing coders to the future of computing. Helped me a lot coming from programming with theoritical knowledge on AI and ML get into creating my own tensorflow models. In late chapters some difficult to understand topics are not explained in depth but that's not the point of the book. SK

    Sentiment
    • Positive
    Content
    • Introduction to TensorFlow
    Dec 04, 2021
  • Incredible, approachable, useful and easy-to-understand approach to TensorFlow and TensorFlow Lite

    I found this book to be incredibly practical, easy-to-approach, and directly applicable to real-world problems that I am trying to solve. I have read quite a few Machine Learning (ML) and AI-type books "for programmers". This book differs from them in having the sole focus of giving programmers the minimum, pertinent, focused information and context needed to start utilizing TensorFlow (TF) on their own projects, right now. There are enough examples in this book that one of them might directly address a problem you are contemplating using TF for. If not, reading how to apply TF across these various scenarios is likely to enable you to try TF in your project. I really like the "results-driven" organization of the chapters. This makes the book both a self-contained class in using TensorFlow as a developer, and a good "cookbook" for both using TensorFlow, and deploying TensorFlow models to alternate/resource-constrained devices (via TensorFlow Lite). I heartily recommend this book for these purposes!

    Sentiment
    • Positive
    Content
    • Practical and Applicable
    Aug 13, 2021
  • If you know how to code, this is the book to get you into AI!

    Laurence is the teacher you always wish you had. He covers every aspect of TensorFlow with deep yet tangible intelligence. If you're worried about understanding the practice of machine learning and artificial intelligence, don't worry any longer. This book guides you through various aspects of the TensorFlow framework, but it also leans into the varying aspects of popular AI problems.

    You get the full tour in this book, from NLP coding to embedding your models in edge devices. Each section is overflowing with code samples to help you solidify what you're learning.

    10 out of 10, a cornerstone of any AI bookshelf.

    Sentiment
    • Positive
    Content
    • Comprehensive Refresher
    Jun 03, 2021
  • Don't you hate it when you can't find the table of contents?

    I have a subscription to online O'Reilly, so I can view the book. It is pretty good as far as having practical details. It is not like it is just a collection of code with no explanation. Usually Amazon shows the table of contents, but not for this one. So here it is:

    Foreword
    Preface
    Who Should Read This Book
    Why I Wrote This Book
    Navigating This Book
    Technology You Need to Understand
    Online Resources
    Conventions Used in This Book
    Using Code Examples
    O’Reilly Online Learning
    How to Contact Us
    Acknowledgments
    I. Building Models
    1. Introduction to TensorFlow
    What Is Machine Learning?
    Limitations of Traditional Programming
    From Programming to Learning
    What Is TensorFlow?
    Using TensorFlow
    Installing TensorFlow in Python
    Using TensorFlow in PyCharm
    Using TensorFlow in Google Colab
    Getting Started with Machine Learning
    Seeing What the Network Learned
    Summary
    2. Introduction to Computer Vision
    Recognizing Clothing Items
    The Data: Fashion MNIST
    Neurons for Vision
    Designing the Neural Network
    The Complete Code
    Training the Neural Network
    Exploring the Model Output
    Training for Longer—Discovering Overfitting
    Stopping Training
    Summary
    3. Going Beyond the Basics: Detecting Features in Images
    Convolutions
    Pooling
    Implementing Convolutional Neural Networks
    Exploring the Convolutional Network
    Building a CNN to Distinguish Between Horses and Humans
    The Horses or Humans Dataset
    The Keras ImageDataGenerator
    CNN Architecture for Horses or Humans
    Adding Validation to the Horses or Humans Dataset
    Testing Horse or Human Images
    Image Augmentation
    Transfer Learning
    Multiclass Classification
    Dropout Regularization
    Summary
    4. Using Public Datasets with TensorFlow Datasets
    Getting Started with TFDS
    Using TFDS with Keras Models
    Loading Specific Versions
    Using Mapping Functions for Augmentation
    Using TensorFlow Addons
    Using Custom Splits
    Understanding TFRecord
    The ETL Process for Managing Data in TensorFlow
    Optimizing the Load Phase
    Parallelizing ETL to Improve Training Performance
    Summary
    5. Introduction to Natural Language Processing
    Encoding Language into Numbers
    Getting Started with Tokenization
    Turning Sentences into Sequences
    Removing Stopwords and Cleaning Text
    Working with Real Data Sources
    Getting Text from TensorFlow Datasets
    Getting Text from CSV Files
    Getting Text from JSON Files
    Summary
    6. Making Sentiment Programmable Using Embeddings
    Establishing Meaning from Words
    A Simple Example: Positives and Negatives
    Going a Little Deeper: Vectors
    Embeddings in TensorFlow
    Building a Sarcasm Detector Using Embeddings
    Reducing Overfitting in Language Models
    Using the Model to Classify a Sentence
    Visualizing the Embeddings
    Using Pretrained Embeddings from TensorFlow Hub
    Summary
    7. Recurrent Neural Networks for Natural Language Processing
    The Basis of Recurrence
    Extending Recurrence for Language
    Creating a Text Classifier with RNNs
    Stacking LSTMs
    Using Pretrained Embeddings with RNNs
    Summary
    8. Using TensorFlow to Create Text
    Turning Sequences into Input Sequences
    Creating the Model
    Generating Text
    Predicting the Next Word
    Compounding Predictions to Generate Text
    Extending the Dataset
    Changing the Model Architecture
    Improving the Data
    Character-Based Encoding
    Summary
    9. Understanding Sequence and Time Series Data
    Common Attributes of Time Series
    Trend
    Seasonality
    Autocorrelation
    Noise
    Techniques for Predicting Time Series
    Naive Prediction to Create a Baseline
    Measuring Prediction Accuracy
    Less Naive: Using Moving Average for Prediction
    Improving the Moving Average Analysis
    Summary
    10. Creating ML Models to Predict Sequences
    Creating a Windowed Dataset
    Creating a Windowed Version of the Time Series Dataset
    Creating and Training a DNN to Fit the Sequence Data
    Evaluating the Results of the DNN
    Exploring the Overall Prediction
    Tuning the Learning Rate
    Exploring Hyperparameter Tuning with Keras Tuner
    Summary
    11. Using Convolutional and Recurrent Methods for Sequence Models
    Convolutions for Sequence Data
    Coding Convolutions
    Experimenting with the Conv1D Hyperparameters
    Using NASA Weather Data
    Reading GISS Data in Python
    Using RNNs for Sequence Modeling
    Exploring a Larger Dataset
    Using Other Recurrent Methods
    Using Dropout
    Using Bidirectional RNNs
    Summary
    II. Using Models
    12. An Introduction to TensorFlow Lite
    What Is TensorFlow Lite?
    Walkthrough: Creating and Converting a Model to TensorFlow Lite
    Step 1. Save the Model
    Step 2. Convert and Save the Model
    Step 3. Load the TFLite Model and Allocate Tensors
    Step 4. Perform the Prediction
    Walkthrough: Transfer Learning an Image Classifier and Converting to TensorFlow Lite
    Step 1. Build and Save the Model
    Step 2. Convert the Model to TensorFlow Lite
    Step 3. Optimize the Model
    Summary
    13. Using TensorFlow Lite in Android Apps
    What Is Android Studio?
    Creating Your First TensorFlow Lite Android App
    Step 1. Create a New Android Project
    Step 2. Edit Your Layout File
    Step 3. Add the TensorFlow Lite Dependencies
    Step 4. Add Your TensorFlow Lite Model
    Step 5. Write the Activity Code to Use TensorFlow Lite for Inference
    Moving Beyond “Hello World”—Processing Images
    TensorFlow Lite Sample Apps
    Summary
    14. Using TensorFlow Lite in iOS Apps
    Creating Your First TensorFlow Lite App with Xcode
    Step 1. Create a Basic iOS App
    Step 2. Add TensorFlow Lite to Your Project
    Step 3. Create the User Interface
    Step 4. Add and Initialize the Model Inference Class
    Step 5. Perform the Inference
    Step 6. Add the Model to Your App
    Step 7. Add the UI Logic
    Moving Beyond “Hello World”—Processing Images
    TensorFlow Lite Sample Apps
    Summary
    15. An Introduction to TensorFlow.js
    What Is TensorFlow.js?
    Installing and Using the Brackets IDE
    Building Your First TensorFlow.js Model
    Creating an Iris Classifier
    Summary
    16. Coding Techniques for Computer Vision in TensorFlow.js
    JavaScript Considerations for TensorFlow Developers
    Building a CNN in JavaScript
    Using Callbacks for Visualization
    Training with the MNIST Dataset
    Running Inference on Images in TensorFlow.js
    Summary
    17. Reusing and Converting Python Models to JavaScript
    Converting Python-Based Models to JavaScript
    Using the Converted Models
    Using Preconverted JavaScript Models
    Using the Toxicity Text Classifier
    Using MobileNet for Image Classification in the Browser
    Using PoseNet
    Summary
    18. Transfer Learning in JavaScript
    Transfer Learning from MobileNet
    Step 1. Download MobileNet and Identify the Layers to Use
    Step 2. Create Your Own Model Architecture with the Outputs from MobileNet as Its Input
    Step 3. Gather and Format the Data
    Step 4. Train the Model
    Step 5. Run Inference with the Model
    Transfer Learning from TensorFlow Hub
    Using Models from TensorFlow.org
    Summary
    19. Deployment with TensorFlow Serving
    What Is TensorFlow Serving?
    Installing TensorFlow Serving
    Installing Using Docker
    Installing Directly on Linux
    Building and Serving a Model
    Exploring Server Configuration
    Summary
    20. AI Ethics, Fairness, and Privacy
    Fairness in Programming
    Fairness in Machine Learning
    Tools for Fairness
    The What-If Tool
    Facets
    Federated Learning
    Step 1. Identify Available Devices for Training
    Step 2. Identify Suitable Available Devices for Training
    Step 3. Deploy a Trainable Model to Your Training Set
    Step 4. Return the Results of the Training to the Server
    Step 5. Deploy the New Master Model to the Clients
    Secure Aggregation with Federated Learning
    Federated Learning with TensorFlow Federated
    Google’s AI Principles
    Summary
    Index

    Sentiment
    • Positive
    Content
    • Practical and Applicable
    Jan 09, 2021
  • Wonderful, easy to read and explained in a simple "Google like" way

    Laurence explains tough concept in easy way.
    Code in Github and website of Laurence has good examples
    This is my 3rd book on Machine Learning, and I have seen different approaches to explaining complex concepts like How CNN works? etc. The stye used by Laurence comes with many decades of experience to understand and I guess thats why he can explain so well. I am liking it :).
    I just finished Chapters 1, 2, and 3 with coding along the way.
    Will update again as I read and practice further.
    Thanks Laurence Moroney.
    Mahtab Syed
    Melbourne

    Sentiment
    • Positive
    Content
    • Clear Explanations
    Jan 07, 2021
  • A really good developer focused intro to Deep Learning with Tensorflow

    If you start with this book, watch Laurence's videos on the TensorFlow/ Google Developer youtube channel and work all the examples and exercises, you will have a solid foundation in deep learning. Then you could potentially learn more from the documentation itself or by using Aurélien Géron's book.
    I feel other Oreilly books should be like this.
    Although math is almost absent in this book (hence 'for coders'), Laurence does a great job explaining convolutions and maxpooling etc.. I've seen lectures from many sources that misattribute pixel/dimension reduction to the convolution step and not the pooling layer (worse yet, lump both together) but the author clearly explains the concepts.

    Sentiment
    • Positive
    Content
    • Introduction to TensorFlow
    Dec 30, 2020
  • Approachable Content as Advertised - Good book

    The book focuses on essential concepts in AI and ML while leaving it to the reader to dig into secondary topics like the math behind various parameters (e.g. stochastic gradient descent, rectified linear unit) or production concerns like frameworks for distributed computing. I would have liked to at least see an appendix with brief lists/discussions of side topics and maybe a table of when to use each algo - not a big deal but could be more catered to the audience than an online search result.

    The concrete examples with increasing complexity are nice. There was some magic hand waving along the way such as selecting the number of nodes in a layer. Perhaps this is the way it works in reality with probing for more ideal numbers through optimizer tools - I don't know.

    My biggest frustration was the pricing of a kindle vs paper book. It seems more fair for me to pay for the IP once and then the production cost of the item I want e.g., $25 for IP plus either $13 for paper and/or $5 for kindle. Paying full price twice to get both is excessive - especially when kindle is cheaper to produce. Because the ETA of the paper book was so much longer, I may never purchase the paper book in this case and therefore everyone loses as I prefer the paper book and they make less money.

    Sentiment
    • Positive
    Content
    • Incomplete Coverage
    Dec 10, 2020
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