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AI: Deep Learning with Python
Section 1 Introduction to Deep Learning
Lecture 1 Welcome (3:07)
Lecture 2 What Is Deep Learning (4:10)
Lecture 3 Open Source Libraries for Deep Learning (4:30)
Lecture 4 Deep Learning Hello World Classifying the MNIST Data (7:45)
Section 2 Backpropagation and Theano for the Rescue
Lecture 5 Introduction to Backpropagation (5:25)
Lecture 6 Understanding Deep Learning with Theano (5:06)
Lecture 7 Optimizing a Simple Model in Pure Theano (7:55)
Section 3 Keras - Making Theano Even Easier to Use
Lecture 8 Keras Behind the Scenes (5:24)
Lecture 9 Fully Connected or Dense Layers (4:45)
Lecture 10 Convolutional and Pooling Layers (6:27)
Section 4 Solving Cats Versus Dogs
Lecture 11 Large Scale Datasets, ImageNet, and Very Deep Neural Networks (5:16)
Lecture 12 Loading Pre-trained Models with Theano (5:01)
Lecture 13 Reusing Pre-trained Models in New Applications (7:07)
Section 5 for Loops and Recurrent Neural Networks in Theano
Lecture 14 Theano for Loops the scan Module (5:18)
Lecture 15 Recurrent Layers (6:30)
Lecture 16 Recurrent Versus Convolutional Layers (3:33)
Lecture 17 Recurrent Networks Training a Sentiment Analysis Model for Text (6:43)
Section 6 Challenge and TensorFlow
Lecture 18 Automatic Image Captioning (4:40)
Lecture 19 Captioning TensorFlow Googles Machine Learning Library (5:09)
Working Files
Working Files
Lecture 4 Deep Learning Hello World Classifying the MNIST Data
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