Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.
Pro Deep Learning with TensorFlowprovides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures.
All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways.
You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.
What You'll Learn
• Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning
• Deploy complex deep learning solutions in production using TensorFlow
• Carry out research on deep learning and perform experiments using TensorFlow
Who This Book Is For
Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts
|Edition description:||1st ed.|
|Product dimensions:||7.01(w) x 10.00(h) x (d)|
About the Author
Santanu Pattanayak currently works at GE, Digital as a Senior Data Scientist. He has 10 years of overall work experience withsix of years of experience in the data analytics/data science field and also has a background in development and database technologies. Prior to joining GE, Santanu worked in companies such as RBS, Capgemini, and IBM. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu is currently pursuing a master's degree in data science from Indian Institute of Technology (IIT), Hyderabad. He also devotes his time to data science hackathons and Kaggle competitions where he ranks within the top 500 across the globe. Santanu was born and brought up in West Bengal, India and currently resides in Bangalore, India with his wife.
Table of Contents
Chapter 1: Machine Learning Basics and Mathematical Foundation for Deep Learning
Chapter Goal: Introduce Machine Learning basics and Mathematical Foundations that are associated with Deep Learning
No of pages 70-90
1. Linear Algebra basics.
2. Numerical Stability and Conditioning.
4. Different types of cost functions and introduction to least squares and maximum likelihood methods.
5. Convex and Non-convex function
6. Optimization Techniques such as Gradient Descent and Shastic Gradient Descent as well as Constrained Optimization problems.
7. Regularization and Early stopping
8. Auto Differentiators and Symbolic Differentiators.
Chapter 2: Introduction to Deep Learning Concepts and TensorFlow
Chapter Goal: Introduce Deep Learning concepts and its comparison with previous Neural Networks. Reasons for its success and computational efficiency and a start to TensorFlow Development.
No of pages 60-70
1. Previous Neural Networks and their shortcomings
2. Introduction to Deep Learning Framework and its advantages.
3. Why TensorFlow for Deep Learning and its comparison with other Deep Learning Frameworks like Theano, Caffe, Torch, etc.
4. Hands on in TensorFlow development environment and introduction to Dynamic Computation graphs.
5. Linear and Logistic regression in a TensorFlow environment
6. Feed forward networks through TensorFlow.
7. Leveraging GPUs for Computational efficiency.
Chapter 3: Image and Audio Processing in TensorFlow through Convolutional Neural Networks
Chapter Goal: Learn to process image and audio data to solve classification, clustering, and recommendation problems using Convolutional Neural Network.
No of pages: 70-80
Sub - Topics:
1. Convolution and Image processing through Convolution.
2. Different Kinds of Image processing filters like Guassian Filter, Sobel Filter, Canny’s edge detection filter.
3. Different Layers of Convolutional Neural Network – Convolution layer, Pooling Layers, activation layers using RELUs, Dropout layers and fully connected layer. Intuition of features learned in Different layers. Concepts of strides, padding and kernels.
4. Solving image classification, clustering and recommendation problems through Convolutional Neural network.
5. Feature transfer in Convolutional Neural Network.
6. Audio classification problems through Convolutional Neural networks.
Chapter 4: Restricted Boltzmann Deep Learning Architectures through TensorFlow for Various Problems
Chapter Goal: Leverage Restricted Boltzmann Machines (RBMs) for solving Recommendation problems, weight initialization in Deep Learning Networks and for Layer by Layer training of Deep Neural Networks.
No of pages:50-60
Sub - Topics:
1. Introduction to Restricted Boltzmann Machines (RBMs) and its architecture.
2. Using RBMs to build Recommendation engines.
3. RBMs for smart weight initialization of Deep Learning Networks.
4. Train complex deep learning networks layer by layer (one layer at a time) through RBMs
Chapter 5: Deep Learning for Natural Language Processing through TensorFlow
Chapter Goal: Leverage TensorFlow Deep learning capabilities for Natural Language processing
No of pages: 50-60
1. Text processing basics such as Word2Vec Representation, Semantic and Syntactic Analysis.
2. Recurrent Neural network(RNNs) for language modelling through TensorFlow
3. Backpropagation through time and problems of Vanishing and Exploding gradients.
4. Gradient Clipping and LSTM (Long Short-Term Memory) to overcome Exploding and Vanishing gradient problems.
5. Applications of RNN in generating sequences and words.
Chapter 6: Unsupervised Learning in TensorFlow through Autoencoders
Chapter Goal: Leverage Autoencoders for doing Unsupervised Learning
No of pages: 30-40
1. Data Compression through Autoencoders.
2. Feature Learning through Auto Encoders.
3. A comparison of feature learning through PCA and Stacked Auto Encoders.