Practical Deep Learning for Cloud, Mobile, and Edge Real-World AI Computer-Vision Projects Using Python, Keras TensorFlow (Anirudh Koul, Siddha Ganju, Meher Kasam) (Z-Library)
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.
Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.
Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral.
Explore fun projects, from Silicon Valley's Not Hotdog app to 40+ industry case studies.
Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning.
Use transfer learning to train models in minutes.
Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.
AI Reading Assistant
Whole-book reading guide from stratified index samples; jump to passages in the text
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Brief outline
【One-Line Pitch】
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobby…
【Book Arc】
- **Opening (~0%–12%)**: If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.; Real-Time Object Classification on iOS with Core ML.
- **Early (~12%–35%)**: Recipe for the Perfect Deep Learning Solution Before Gordon Ramsay starts cooking, he ensures he has all of the ingredients ready to go.; yer over other libraries, making rapid prototyping possible.
- **Middle (~35%–65%)**: curacy for each PCA dimension') plt.xlabel('Test Time') plt.ylabel('Accuracy') plt.grid(True) plt.show() Figure 4-14.; The downside with that approach is that it’s not real time.
- **Late (~65%–88%)**: t hundreds of images in three minutes or less with a Chrome extension called Fatkun Batch Download Image.; arison to images that the model can already easily classify.
- **Ending (~88%–100%)**: Bringing our trained model to the browser is an easy task.; // PoseNet callback function poseNet.on('pose', function (results) { const poses results;
【Key Takeaways】
- **If your goal is to bui…** (Opening): If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.
- **Real-Time Object Class…** (Opening): Real-Time Object Classification on iOS with Core ML.
- **And then we pit them a…** (Opening): And then we pit them against each other in an open benchmark —you might be surprised who won.
- **Recipe for the Perfect…** (Early): Recipe for the Perfect Deep Learning Solution Before Gordon Ramsay starts cooking, he ensures he has all of the ingredients ready to go.
- **yer over other libraries** (Early): yer over other libraries, making rapid prototyping possible.
- **a pretrained model** (Early): a pretrained model, remove the last few task-specific lay‐ ers, and append a new classifier layer.
【Reading Tips】
- Use Passage locations below to jump into the text and set reading anchors
- If this is a brief outline, click Regenerate (top right) for a synthesized guide
【Coverage Limits】
Compressed outline without the model (~33 index chunks). Full structured guide needs AI available.
Page 11
ck 265 Summary 266 10. AI in the Browser with TensorFlow.js and ml5.js. . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 JavaScript-Based Machine Le...
yer over other libraries, making rapid prototyping possible. This made the learning curve a lot less steep for beginners of deep learning. At the same time,...
training images. Not bad! And by the 10th step, we observe about 97% validation accuracy. That’s the power of transfer learning. Let us take a moment to appr...
None) # Passing the image through Grad CAM grid = explainer.explain(data, model, 'conv1', index) name = IMAGE_PATH.split(".jpg")[0] explainer.save(grid, '/tm...
rds, until it begins to perform well on the training set. 3. Train on a larger sample (or even the entire training set). 172 | Chapter 6: Maximizing Speed an...
od balance between precision and recall across all services. At a high confidence threshold such as 0.99, a classifier might be very accurate, but there migh...
silly. You like to dream big. How about 100 million people? That’s a nice round number. Now con‐ vincing 100 million people to download and install an app an...
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Practical Deep Learning for Cloud, Mobile, and Edge Real-World AI Computer-Vision Projects Using Python, Keras TensorFlow (Anirudh Koul, Siddha Ganju, Meher Kasam) (Z-Library)
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