Python Deep Learning Projects
This is the code repository for Python Deep Learning Projects, published by Packt.
9 projects demystifying neural network and deep learning models for building intelligent systems
If you feel this book is for you, get your copy today!
ISBN 13: 9781788997096
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,
Google Cloud are used to showcase the strengths of various approaches covering
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.
Table of content
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 – [Python 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]
Feel Free to contact us if you have any question:
Matthew Lamons's background is in experimental psychology and deep learning. Founder and CEO of Skejul—the AI platform to help people manage their activities. Named by Gartner, Inc. as a "Cool Vendor" in the "Cool Vendors in Unified Communication, 2017" report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate AI tech startups based on the success of his Applied AI MasterMinds group. Matthew's global community of more than 85 K are leaders in AI, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision, reinforcement, and deep learning. Matthew invites you to join him on his mission to simplify the future and to build AI for good.
- LinkedIn : https://www.linkedin.com/in/matthew-lamons/
Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher. His expertise in building multilingual NLU systems and large-scale AI infrastructures has brought him to Copenhagen, where he leads a large team of AI engineers as Chief AI Scientist at Jatana. Often invited to speak at AI conferences, he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he's currently involved in various research projects on Quantum Computing (QC), high-performance computing (HPC), and the brain-computer interaction (BCI).
- Github: https://github.com/goodrahstar
- LinkedIn : https://www.linkedin.com/in/hellorahlk/
- Medium : https://medium.com/@hellorahulk
- Twitter : https://twitter.com/hellorahulk
Abhishek Nagaraja was born and raised in India. Graduated Magna Cum Laude from the University of Illinois at Chicago, United States, with a Masters Degree in Mechanical Engineering with a concentration in Mechatronics and Data Science. Abhishek specializes in Keras and TensorFlow for building and evaluation of custom architectures in deep learning recommendation models. His deep learning skills and interest span computational linguistics and NLP to build chatbots to computer vision and reinforcement learning. He has been working as a Data Scientist for Skejul Inc. building an AI-powered activity forecast engine and engaged as a Deep Learning Data Scientist with The Intelligence Factory building solutions for enterprise clients.
- LinkedIn : https://www.linkedin.com/in/abhishek-nagaraja-4325aa110/
- Github: https://github.com/anagar20