If you are a machine learning engineer, data scientist, AI developer, or anyone who wants to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find this book helpful.
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Section 1: Getting Started with Deep Learning
- Introduction to Deep Learning
- Getting to Know TensorFlow
- What is TensorFlow?
- Understanding computational graphs and sessions
- Variables, constants, and placeholders
- Introducing TensorBoard
- Handwritten digit classification using TensorFlow
- Introducing eager execution
- Math operations in TensorFlow
- TensorFlow 2.0 and Keras
- Should we use Keras or TensorFlow?
- Summary
- Questions
- Further reading
- Section 2: Fundamental Deep Learning Algorithms
- Gradient Descent and Its Variants
- Generating Song Lyrics Using RNN
- Improvements to the RNN
- LSTM to the rescue
- Gated recurrent units
- Bidirectional RNN
- Going deep with deep RNN
- Language translation using the seq2seq model
- Summary
- Questions
- Further reading
- Demystifying Convolutional Networks
- Learning Text Representations
- Understanding the word2vec model
- Building the word2vec model using gensim
- Visualizing word embeddings in TensorBoard
- Doc2vec
- Understanding skip-thoughts algorithm
- Quick-thoughts for sentence embeddings
- Summary
- Questions
- Further reading
- Section 3: Advanced Deep Learning Algorithms
- Generating Images Using GANs
- Differences between discriminative and generative models
- Say hello to GANs!
- DCGAN – Adding convolution to a GAN
- Least squares GAN
- GANs with Wasserstein distance
- Summary
- Questions
- Further reading
- Learning More about GANs
- Reconstructing Inputs Using Autoencoders
- Exploring Few-Shot Learning Algorithms
- Assessments
- Chapter 1 - Introduction to Deep Learning
- Chapter 2 - Getting to Know TensorFlow
- Chapter 3 - Gradient Descent and Its Variants
- Chapter 4 - Generating Song Lyrics Using an RNN 
- Chapter 5 - Improvements to the RNN
- Chapter 6 - Demystifying Convolutional Networks
- Chapter 7 - Learning Text Representations
- Chapter 8 - Generating Images Using GANs
- Chapter 9 - Learning More about GANs
- Chapter 10 - Reconstructing Inputs Using Autoencoders
- Chapter 11 - Exploring Few-Shot Learning Algorithms
- Other Books You May Enjoy
Who this book is for
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