Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow Learning Deep Learning: Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, an
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You Will Learn About
Perceptron and other artificial neurons
They are the fundamental building blocks of deep neural networks that have enabled the DL revolution.
Fully connected feedforward networks and convolutional networks
You will apply these networks to solve practical problems, such as predicting housing prices based on a large number of variables or identifying to which category an image belongs.
Ways to represent words from a natural language using an encoding
Encoding captures some of the semantics of the encoded words. Encodings are used together with a recurrent neural network to create a neural-based natural language translator. This translator can automatically translate simple sentences from English to French or other similar languages.
Building an image-captioning network
Networks that combines image and language processing. This network takes an image as an input and automatically generates a natural language description of the image.