This is the code repository for The Deep Learning Architect’s Handbook, published by Packt.
Build and deploy production-ready DL solutions leveraging the latest Python techniques
Deep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives.
This book covers the following exciting features:
- Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs)
- Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model
- Deal with multi-modal data drift in a production environment
- Evaluate the quality and bias of your models
- Explore techniques to protect your model from adversarial attacks
- Get to grips with deploying a model with DataRobot AutoML
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from lingua import Language, LanguageDetectorBuilder
tqdm.pandas()
Following is what you need for this book: This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-19).
The code provided in the chapters has been tested on a computer with Python 3.10, Ubuntu 20.04 LTS 64-bit OS, 32 GB RAM, and an RTX 2080TI GPU for running deep learning models. Although the code has been tested on this specific setup, it may also work on other configurations; however, compatibility and performance are not guaranteed. Python dependencies are included in the requirements.txt file for easy installation in each chapter’s respective GitHub folders. Additionally, some non-Python software might be required; their installation instructions will be mentioned at the beginning of each relevant tutorial. For these software installations, you need to refer to external manuals or guides to install them. Do keep in mind the potential differences in system configurations as you carry out the practical code sections in this book.
Ee Kin Chin Ee Kin Chin is a senior deep learning engineer at DataRobot. He led teams to develop advanced AI tools used by numerous organizations from diverse industries and provided consultation on many customer AI use cases. Previously, he worked on deep learning (DL) computer vision projects for smart vehicles and human sensing applications at Panasonic and offered AI solutions using edge cameras at a tech solutions provider. He was also a DL mentor for an online course. Holding a Bachelor of Engineering (honors) degree in electronics, with a major in telecommunications, and a proven track record of successful application of AI, Ee Kin's expertise includes embedded applications, practical deep learning, data science, and classical machine learning.