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About Abhishek Thakur
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The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option.
This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.
Table of contents:
- Setting up your working environment
- Supervised vs unsupervised learning
- Evaluation metrics
- Arranging machine learning projects
- Approaching categorical variables
- Feature engineering
- Feature selection
- Hyperparameter optimization
- Approaching image classification & segmentation
- Approaching text classification/regression
- Approaching ensembling and stacking
- Approaching reproducible code & model serving
There are no sub-headings. Important terms are written in bold.
I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, create an issue in GitHub repository: https://github.com/abhishekkrthakur/approachingalmost
And Subscribe to my youtube channel: https://bit.ly/abhitubesub
Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios
- Build efficient deep learning pipelines using the popular Tensorflow framework
- Train neural networks such as ConvNets, generative models, and LSTMs
- Includes projects related to Computer Vision, stock prediction, chatbots and more
TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects.
TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
What you will learn
- Set up the TensorFlow environment for deep learning
- Construct your own ConvNets for effective image processing
- Use LSTMs for image caption generation
- Forecast stock prediction accurately with an LSTM architecture
- Learn what semantic matching is by detecting duplicate Quora questions
- Set up an AWS instance with TensorFlow to train GANs
- Train and set up a chatbot to understand and interpret human input
- Build an AI capable of playing a video game by itself –and win it!
Who this book is for
This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
Table of Contents
- Recognizing traffic signs using Convnets
- Annotating Images with Object Detection API
- Caption generation for images
- Building GANs for Conditional Image Creation
- Stock Price Prediction with LSTM
- Create & Train Machine Translation Systems
- Train and set up a Chatbot, able to discuss like a human
- Detecting Duplicate Quora Questions
- Building a TensorFlow Recommender Systems
- Video Games by Reinforcement learning