# MasteringPyTorchV2
**Repository Path**: whs075/MasteringPyTorchV2
## Basic Information
- **Project Name**: MasteringPyTorchV2
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-05-13
- **Last Updated**: 2025-05-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Mastering PyTorch, Second Edition
This is the code repository for the book [Mastering PyTorch, Second Edition](https://www.amazon.com/Mastering-PyTorch-powerful-learning-architectures-ebook/dp/1801074305), published by Packt.
# What is this book about?
PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models.
# What you will learn
* Implement text, vision, and music generation models using PyTorch
* Build a deep Q-network (DQN) model in PyTorch
* Deploy PyTorch models on mobile devices (Android and iOS)
* Become well versed in rapid prototyping using PyTorch with fastai
* Perform neural architecture search effectively using AutoML
* Easily interpret machine learning models using Captum
* Design ResNets, LSTMs, and graph neural networks (GNNs)
* Create language and vision transformer models using Hugging Face
# Who This Book Is for?
This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
# Notebooks in each chapter
You can run the notebooks directly from the table below:
| Chapter no. |Chapter title | Notebook/Utility Script (GitHub) | Open in Kaggle | Open in Colab
|:-- |:-- | :-------- | :-------- | :-------- |
| 1 | Overview of Deep Learning Using PyTorch | [mnist_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_pytorch.ipynb) |
| | | [mnist_tensorflow.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter01/mnist_tensorflow.ipynb) |
| 2 | Deep CNN Architectures | [DenseNetBlock.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/DenseNetBlock.ipynb) |
| | | [GoogLeNet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/GoogLeNet.ipynb) |
| | | [ResNetBlock.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/ResNetBlock.ipynb) |
| | | [lenet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/lenet.ipynb) |
| | | [transfer_learning_alexnet.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/transfer_learning_alexnet.ipynb) |
| | | [vgg13_pretrained_run_inference.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter02/vgg13_pretrained_run_inference.ipynb) |
| 3 | Combining CNNs and LSTMs | [image_captioning_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter03/image_captioning_pytorch.ipynb) |
| 4 | Deep Recurrent Model Architectures | [lstm.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter04/lstm.ipynb) |
| | | [rnn.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter04/rnn.ipynb) |
| 5 | Advanced Hybrid Models | [out_of_the_box_transformers.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/out_of_the_box_transformers.ipynb) |
| | | [rand_wire_nn.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/rand_wire_nn.ipynb) |
| | | [transformer.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter05/transformer.ipynb) |
| 6 | Graph Neural Networks | [GNN.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter06/GNN.ipynb) |
| 7 | Music and Text Generation with PyTorch | [music_generation.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/music_generation.ipynb) |
| | | [text_generation.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation.ipynb) |
| | | [text_generation_out_of_the_box.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box.ipynb) |
| | | [text_generation_out_of_the_box_gpt3.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter07/text_generation_out_of_the_box_gpt3.ipynb) |
| 8 | Neural Style Transfer | [neural_style_transfer.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter08/neural_style_transfer.ipynb) |
| 9 | Deep Convolutional GANs | [dcgan.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter09/dcgan.ipynb) |
| | | [pix2pix_architecture.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter09/pix2pix_architecture.ipynb) |
| 10 | Image Generation Using Diffusion | [image_generation_using_diffusion.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/image_generation_using_diffusion.ipynb) |
| | | [taj_mahal_image.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/taj_mahal_image.ipynb) |
| | | [text_to_image_generation_using_stable_diffusion_v1_5.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter10/text_to_image_generation_using_stable_diffusion_v1_5.ipynb) |
| 11 | Deep Reinforcement Learning | [pong.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter11/pong.ipynb) |
| 13 | Operationalizing PyTorch Models into Production | [mnist_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/mnist_pytorch.ipynb) |
| | | [model_scripting.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_scripting.ipynb) |
| | | [model_tracing.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/model_tracing.ipynb) |
| | | [onnx.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/onnx.ipynb) |
| | | [run_inference.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter13/run_inference.ipynb) |
| 15 | Rapid Prototyping with PyTorch | [fastai.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/fastai.ipynb) |
| | | [poutyne.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/poutyne.ipynb) |
| | | [pytorch_lightning.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_lightning.ipynb) |
| | | [pytorch_profiler.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter15/pytorch_profiler.ipynb) |
| 16 | PyTorch and AutoML | [automl-pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter16/automl-pytorch.ipynb) |
| | | [optuna_pytorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter16/optuna_pytorch.ipynb) |
| 17 | PyTorch and Explainable AI | [captum_interpretability.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter17/captum_interpretability.ipynb) |
| | | [pytorch_interpretability.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter17/pytorch_interpretability.ipynb) |
| 18 | Recommendation Systems with PyTorch | [torch-recsys.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter18/torch-recsys.ipynb) |
| 19 | PyTorch and Hugging Face | [HuggingFaceAccelerate.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceAccelerate.ipynb) |
| | | [HuggingFaceDatasets.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceDatasets.ipynb) |
| | | [HuggingFaceHub.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceHub.ipynb) |
| | | [HuggingFaceOptimum.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFaceOptimum.ipynb) |
| | | [HuggingFacePyTorch.ipynb](https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) | [](https://kaggle.com/kernels/welcome?src=https://github.com/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) | [](https://colab.research.google.com/github/arj7192/MasteringPyTorchV2/blob/main/Chapter19/HuggingFacePyTorch.ipynb) |
## Know more on the Discord server
You can get more engaged on the discord server for more latest updates and discussions in the community at [Discord](https://packt.link/mastorch)
## Download a free PDF
_If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF._
[Free-Ebook](https://download.packt.com/free-ebook/9781801074308)
We also provide a PDF file that has color images of the screenshots/diagrams used in this book at [GraphicBundle](https://packt.link/gbp/9781801074308)
## Get to know the Author
_Ashish Ranjan Jha_ studied electrical engineering at IIT Roorkee, computer science at École Polytechnique
Fédérale de Lausanne (EPFL), and he also completed his MBA at Quantic School of Business,
with a distinction in all three degrees. He has worked for bigger tech companies like Oracle and Sony,
and recent tech unicorns – Revolut and Tractable, in the fields of data science, machine learning and
artificial intelligence. He currently works as head of ML and AI at XYZ Reality, based in London (a
construction tech start-up where construction meets AR/VR meets ML/AI to enable real-time data
driven construction intelligence). He is also an advisor to SUIND, an agritech startup that uses drones
for intelligence. Along with that, he has also authored a book, _Fight Fraud with Machine Learning_.
## Other Related Books
- [Machine Learning with PyTorch and Scikit-Learn](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312)
- [Python Data Cleaning Cookbook – Second Edition](https://www.packtpub.com/product/python-data-cleaning-cookbook-second-edition/9781803239873)