Deep Learning with Python by François Chollet

The book starts with some nice background to machine learning. This is a roundabout way of saying we should expect Chollet to be expert and authoritative in this area. Google is also the home of Tensorflow, a lower level library which is often used as a backend to Keras. Neural networks have been used in production since the 1990s – by the US postal service for reading handwritten zip codes.Ĭhollet works on artificial intelligence at Google and is the author of the Keras deep learning library. The area has boomed in the last few years with the availability of massive datasets on which to train, improvements in numerical algorithms for training neural networks and the use of GPUs to further accelerate deep learning. This is where the “deep” comes in – it refers to the numbers of layers in the networks. The biggest innovations I see from this book are in the use of pre-trained networks, and the dominance of the Keras/Tensorflow/Python ecosystem in doing deep learning.ĭeep learning is a type of artificial intelligence based on many-layered neural networks.

Despite these reviews only spanning a couple of years it feels like the area is moving on rapidly. Plt.Deep learning with Python by Francois Chollet is the third book I have reviewed on deep learning neural networks. Should read plt.plot(epochs, acc_values, 'bo', label= 'Training acc') Plt.plot(epochs, val_acc, 'b', label= 'Validation acc') Listing 3.10 Plotting the training and validation accuracy plt.plot(epochs, acc, 'bo', label= 'Training acc') Should read epochs = range( 1, len(val_loss_values) + 1)ģ. Another error is the code in Listing 3.9 Plotting the training and validation loss. If you have 'Francois Chollet' contact, please help confirm with him if there is an error. Kernel is a weights matrix created by the layer,Īs you see, the first tensor in dot function should be input, not Weight. Output = activation(dot(input, kernel) + bias) where activation is the element-wise activation function passed as the activation argument,

I search the reference and found that my understanding is correct The equation above doesn't make sense for me because all features should be multiplied by W, so that the equation should look like output = relu( dot(input,W) + b) However I found error and hope this article would improve this book.

This book fill the gap after I finish Machine Learning Crash Course. Deep Learning with Python by Francois Chollet is great book in Machine Learning and Deep Learning.

Deep Learning with Python by François Chollet