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.
My book is missing pages 1-15.
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.
- 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.
- 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.
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
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!
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.
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:
Who Should Read This Book
Why I Wrote This Book
Navigating This Book
Technology You Need to Understand
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
I. Building Models
1. Introduction to TensorFlow
What Is Machine Learning?
Limitations of Traditional Programming
From Programming to Learning
What Is TensorFlow?
Installing TensorFlow in Python
Using TensorFlow in PyCharm
Using TensorFlow in Google Colab
Getting Started with Machine Learning
Seeing What the Network Learned
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
3. Going Beyond the Basics: Detecting Features in Images
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
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
The ETL Process for Managing Data in TensorFlow
Optimizing the Load Phase
Parallelizing ETL to Improve Training Performance
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
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
7. Recurrent Neural Networks for Natural Language Processing
The Basis of Recurrence
Extending Recurrence for Language
Creating a Text Classifier with RNNs
Using Pretrained Embeddings with RNNs
8. Using TensorFlow to Create Text
Turning Sequences into Input Sequences
Creating the Model
Predicting the Next Word
Compounding Predictions to Generate Text
Extending the Dataset
Changing the Model Architecture
Improving the Data
9. Understanding Sequence and Time Series Data
Common Attributes of Time Series
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
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
11. Using Convolutional and Recurrent Methods for Sequence Models
Convolutions for Sequence Data
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 Bidirectional RNNs
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
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
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
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
16. Coding Techniques for Computer Vision in TensorFlow.js
Using Callbacks for Visualization
Training with the MNIST Dataset
Running Inference on Images in TensorFlow.js
Using the Converted Models
Using the Toxicity Text Classifier
Using MobileNet for Image Classification in the Browser
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
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
20. AI Ethics, Fairness, and Privacy
Fairness in Programming
Fairness in Machine Learning
Tools for Fairness
The What-If Tool
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
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.
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.
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.
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