Neural Network Programming with TensorFlow
Unleash the power of TensorFlow to train efficient neural networks
1st Edition
Published on 8. July 2025
274 pages
978-1-78839-775-9 (ISBN)
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Ghotra Manpreet Singh :
Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.Dua Rajdeep :
Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.Dua Rajdeep :
Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.
Content
- Cover
- Copyright
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Customer Feedback
- Table of Contents
- Preface
- Chapter 1: Maths for Neural Networks
- Understanding linear algebra
- Environment setup
- Setting up the Python environment in Pycharm
- Linear algebra structures
- Scalars, vectors, and matrices
- Tensors
- Operations
- Vectors
- Matrices
- Matrix multiplication
- Trace operator
- Matrix transpose
- Matrix diagonals
- Identity matrix
- Inverse matrix
- Solving linear equations
- Singular value decomposition
- Eigenvalue decomposition
- Principal Component Analysis
- Calculus
- Gradient
- Hessian
- Determinant
- Optimization
- Optimizers
- Summary
- Chapter 2: Deep Feedforward Networks
- Defining feedforward networks
- Understanding backpropagation
- Implementing feedforward networks with TensorFlow
- Analyzing the Iris dataset
- Code execution
- Implementing feedforward networks with images
- Analyzing the effect of activation functions on the feedforward networks accuracy
- Summary
- Chapter 3: Optimization for Neural Networks
- What is optimization?
- Types of optimizers
- Gradient descent
- Different variants of gradient descent
- Algorithms to optimize gradient descent
- Which optimizer to choose
- Optimization with an example
- Summary
- Chapter 4: Convolutional Neural Networks
- An overview and the intuition of CNN
- Single Conv Layer Computation
- CNN in TensorFlow
- Image loading in TensorFlow
- Convolution operations
- Convolution on an image
- Strides
- Pooling
- Max pool
- Example code
- Average pool
- Image classification with convolutional networks
- Defining a tensor for input images and the first convolution layer
- Input tensor
- First convolution layer
- Second convolution layer
- Third convolution layer
- Flatten the layer
- Fully connected layers
- Defining cost and optimizer
- Optimizer
- First epoch
- Plotting filters and their effects on an image
- Summary
- Chapter 5: Recurrent Neural Networks
- Introduction to RNNs
- RNN implementation
- Computational graph
- RNN implementation with TensorFlow
- Computational graph
- Introduction to long short term memory networks
- Life cycle of LSTM
- LSTM implementation
- Computational graph
- Sentiment analysis
- Word embeddings
- Sentiment analysis with an RNN
- Computational graph
- Summary
- Chapter 6: Generative Models
- Generative models
- Discriminative versus generative models
- Types of generative models
- Autoencoders
- GAN
- Sequence models
- GANs
- GAN with an example
- Types of GANs
- Vanilla GAN
- Conditional GAN
- Info GAN
- Wasserstein GAN
- Coupled GAN
- Summary
- Chapter 7: Deep Belief Networking
- Understanding deep belief networks
- DBN implementation
- Class initialization
- RBM class
- Pretraining the DBN
- Model training
- Predicting the label
- Finding the accuracy of the model
- DBN implementation for the MNIST dataset
- Loading the dataset
- Input parameters for a DBN with 256-Neuron RBM layers
- Output for a DBN with 256-neuron RBN layers
- Effect of the number of neurons in an RBM layer in a DBN
- An RBM layer with 512 neurons
- An RBM layer with 128 neurons
- Comparing the accuracy metrics
- DBNs with two RBM layers
- Classifying the NotMNIST dataset with a DBN
- Summary
- Chapter 8: Autoencoders
- Autoencoder algorithms
- Under-complete autoencoders
- Dataset
- Basic autoencoders
- Autoencoder initialization
- AutoEncoder class
- Basic autoencoders with MNIST data
- Basic autoencoder plot of weights
- Basic autoencoder recreated images plot
- Basic autoencoder full code listing
- Basic autoencoder summary
- Additive Gaussian Noise autoencoder
- Autoencoder class
- Additive Gaussian Autoencoder with the MNIST dataset
- Training the model
- Plotting the weights
- Plotting the reconstructed images
- Additive Gaussian autoencoder full code listing
- Comparing basic encoder costs with the Additive Gaussian Noise autoencoder
- Additive Gaussian Noise autoencoder summary
- Sparse autoencoder
- KL divergence
- KL divergence in TensorFlow
- Cost of a sparse autoencoder based on KL Divergence
- Complete code listing of the sparse autoencoder
- Sparse autoencoder on MNIST data
- Comparing the Sparse encoder with the Additive Gaussian Noise encoder
- Summary
- Chapter 9: Research in Neural Networks
- Avoiding overfitting in neural networks
- Problem statement
- Solution
- Results
- Large-scale video processing with neural networks
- Resolution improvements
- Feature histogram baselines
- Quantitative results
- Named entity recognition using a twisted neural network
- Example of a named entity recognition
- Defining Twinet
- Results
- Bidirectional RNNs
- BRNN on TIMIT dataset
- Summary
- Appendix: Getting started with TensorFlow
- Environment setup
- TensorFlow comparison with Numpy
- Computational graph
- Graph
- Session objects
- Variables
- Scope
- Data input
- Placeholders and feed dictionaries
- Auto differentiation
- TensorBoard
- Index
