Deep Learning with Applications Using Python  [electronic resource] : Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras /
Manaswi, Navin Kumar.author.
Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning.
1. Basics of Tensorflow -- 2. Basics of Keras -- 3. Multilayered Perceptron -- 4. Regression to MLP in Tensorflow -- 5. Regression to MLP in Keras -- 6. CNN in Visuals -- 7. CNN with Tensorflow -- 8. CNN with Keras -- 9. RNN and LSTM  -- 10. Speech to Text and Vice Versa -- 11. Developing Chatbots -- 12. Face Detection and Face Recognition. .
Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning.
Artificial intelligence.
Python (Computer program language).
Big data.
Artificial Intelligence.
Python.
Big Data.