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amanpro30amanpro30
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Image Rendering problem resolved (aimacode#1178)
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‎notebooks/chapter19/Learners.ipynb

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"\n",
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"By default we use dense networks with two hidden layers, which has the architecture as the following:\n",
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"\n",
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"<img src=\"images/nn.png\" width=500/>\n",
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"<img src=\"images/nn.png\" width=\"500\"/>\n",
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"\n",
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"In our code, we implemented it as:"
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,

‎notebooks/chapter19/Loss Functions and Layers.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src= images/mse_plot.png width=500/>"
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"<img src=\"images/mse_plot.png\" width=\"500\"/>"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src= images/corss_entropy_plot.png width=500/>"
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"<img src=\"images/corss_entropy_plot.png\" width=\"500\"/>"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,

‎notebooks/chapter19/Optimizer and Backpropagation.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/backprop.png\" width=500/>"
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"<img src=\"images/backprop.png\" width=\"500\"/>"
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{
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"source": [
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"Applying optimizers and back-propagation algorithm together, we can update the weights of a neural network to minimize the loss function with alternatively doing forward and back-propagation process. Here is a figure form [here](https://medium.com/datathings/neural-networks-and-backpropagation-explained-in-a-simple-way-f540a3611f5e) describing how a neural network updates its weights:\n",
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"\n",
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"<img src=\"images/nn_steps.png\" width=700></img>"
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"<img src=\"images/nn_steps.png\" width=\"700\"></img>"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,

‎notebooks/chapter19/RNN.ipynb

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"\n",
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"Recurrent neural networks address this issue. They are networks with loops in them, allowing information to persist.\n",
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"<img src=\"images/rnn_unit.png\" width=500/>"
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"<img src=\"images/rnn_unit.png\" width=\"500\"/>"
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]
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"source": [
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"A recurrent neural network can be thought of as multiple copies of the same network, each passing a message to a successor. Consider what happens if we unroll the above loop:\n",
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" \n",
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"<img src=\"images/rnn_units.png\" width=500/>"
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"<img src=\"images/rnn_units.png\" width=\"500\"/>"
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"source": [
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"As demonstrated in the book, recurrent neural networks may be connected in many different ways: sequences in the input, the output, or in the most general case both.\n",
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"\n",
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"<img src=\"images/rnn_connections.png\" width=700/>"
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"<img src=\"images/rnn_connections.png\" width=\"700\"/>"
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{
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"\n",
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"Autoencoders are an unsupervised learning technique in which we leverage neural networks for the task of representation learning. It works by compressing the input into a latent-space representation, to do transformations on the data. \n",
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"\n",
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"<img src=\"images/autoencoder.png\" width=800/>"
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"<img src=\"images/autoencoder.png\" width=\"800\"/>"
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"\n",
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"Autoencoders have different architectures for different kinds of data. Here we only provide a simple example of a vanilla encoder, which means they're only one hidden layer in the network:\n",
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"\n",
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"<img src=\"images/vanilla.png\" width=500/>\n",
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"<img src=\"images/vanilla.png\" width=\"500\"/>\n",
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"\n",
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"You can view the source code by:"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.8"
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"version": "3.6.9"
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}
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"nbformat": 4,

‎notebooks/chapter24/Image Edge Detection.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/gradients.png\" width=700/>"
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"<img src=\"images/gradients.png\" width=\"700\"/>"
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"cell_type": "markdown",
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"source": [
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"<img src=\"images/stapler.png\" width=500/>\n",
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"<img src=\"images/stapler.png\" width=\"500\"/>\n",
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"\n",
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"We will use `matplotlib` to read the image as a numpy ndarray:"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/derivative_of_gaussian.png\" width=400/>"
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"<img src=\"images/derivative_of_gaussian.png\" width=\"400\"/>"
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{
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"cell_type": "markdown",
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"source": [
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"<img src=\"images/laplacian.png\" width=200/>"
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"<img src=\"images/laplacian.png\" width=\"200\"/>"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<img src=\"images/laplacian_kernels.png\" width=300/>"
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"<img src=\"images/laplacian_kernels.png\" width=\"300\"/>"
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]
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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"version": "3.6.9"
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}
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"nbformat": 4,

‎notebooks/chapter24/Objects in Images.ipynb

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"source": [
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"The bounding boxes are drawn on the original picture showed in the following:\n",
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"\n",
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"<img src=\"images/stapler_bbox.png\" width=500/>"
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"<img src=\"images/stapler_bbox.png\" width=\"500\"/>"
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"[Ross Girshick et al.](https://arxiv.org/pdf/1311.2524.pdf) proposed a method where they use selective search to extract just 2000 regions from the image. Then the regions in bounding boxes are feed into a convolutional neural network to perform classification. The brief architecture can be shown as:\n",
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"\n",
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"<img src=\"images/RCNN.png\" width=500/>"
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"<img src=\"images/RCNN.png\" width=\"500\"/>"
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.2"
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"version": "3.6.9"
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}
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},
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"nbformat": 4,

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