ISBN 13: 9781788997096
Authors: Rahul Kumar & Matt Fanli Ramsey & Abhishek Nagaraja
This book is aimed at intermediate machine learning engineers, software engineers, technology architects and programmers who are interested in knowing more about deep learning, especially applied deep learning, TensorFlow, Google Cloud and Keras. Python Deep Learning Projects is focused at the core of the data science pipeline – model building, training, evaluation, and validation.
We approach deep learning projects from a very practical point of view. In thinking about how to share what we know, our experiences, the strategies that we've learned, and the tactics we employed in our daily day to day life.
Tools and frameworks like, Keras, TensorFlow, and Google Cloud are used to showcase the strengths of various approaches covering NLP, CV and ASR.
- Link to Packt Publishing: http://bit.ly/DeepLearningProjects
- Link to Amazon: https://www.amazon.com/Python-Deep-Learning-Projects-Architectures/dp/1788997093
This course is for intermediate machine learners like if you've undertaken at least one course in machine learning and have a modest functional proficiency in Python (meaning you can create programs in Python when supported by examples). Many of our readers will be undergraduates at university studying computer science, statistics, mathematics, physics, biology, chemistry, marketing, and business.
You will be successful in this course if you have a basic knowledge of computer programming especially Python programming language. Also some familiarity with deep learning like neural networks will be helpful.
In this course, you will need a Google Cloud free tier account. Note that you won't be charged by creating the account. Instead, you can get $300 credit to spend on Google Cloud Platform for 12 months and access to the Always Free tier to try participating products at no charge. By going through this course, you will probably need to spend at most $50 out of your $300 free credit.
SECTION I – [Python deep learning – building the foundation]
- Chapter 1 : Building Deep Learning Environment
- Chapter 2 : Training NN for Prediction using Regression
SECTION II – [Python deep learning – NLP]
- Chapter 3 : Word representation using word2vec
- Chapter 4 : Build NLP pipeline for building chatbots
- Chapter 5 : Sequence-to-sequence models for building chatbots
- Chapter 6 : Generative Language Model for Content Creation
- Chapter 7 : Building Speech Recognition with DeepSpeech2
SECTION II – [Deep learning – Computer Vision]
- Chapter 8 : Handwritten Digits Classification Using ConvNets
- Chapter 9 : Object Detection using OpenCV and TensorFlow
- Chapter 10: Building Face Recognition using Facenet
- Chapter 11: Automated Image Captioning
- Chapter 12: Pose Estimation on 3D models using ConvNets
- Chapter 13: Image translation using GANs for style transfer
SECTION IV – [Python deep learning – Reinforcement Learning]
Matthew Lamons
- LinkedIn : https://www.linkedin.com/in/matthew-lamons/
Rahul Kumar
- Github: https://github.com/goodrahstar
- LinkedIn : https://www.linkedin.com/in/hellorahlk/
- Medium : https://medium.com/@hellorahulk
- Twitter : https://twitter.com/hellorahulk
Abhishek Nagaraja
- LinkedIn : https://www.linkedin.com/in/abhishek-nagaraja-4325aa110/
- Github: https://github.com/anagar20
