contents why deep learningwhy now hardware data algorithms new wave of investment the democratization of deep learning will it last we beginthe mathematical building blocks of before neural networks first look at neural network data representations for neural networks scalars ( tensors vectors ( tensors matrices ( tensors tensors and higherdimensional tensors key attributes manipulating tensors in numpy the notion of data batches real-world examples of data tensors vector data timeseries data or sequence data image data video data the gears of neural networkstensor operations element-wise operations broadcasting tensor dot tensor reshaping geometric interpretation of tensor operations geometric interpretation of deep learning the engine of neural networksgradient-based optimization what' derivative derivative of tensor operationthe gradient stochastic gradient descent chaining derivativesthe backpropagation algorithm looking back at our first example summary started with neural networks getting anatomy of neural network layersthe building blocks of deep learning modelsnetworks of layers loss functions and optimizerskeys to configuring the learning process introduction to keras kerastensorflowtheanoand cntk with kerasa quick overview setting up deep-learning workstation developing jupyter notebooksthe preferred way to run deep-learning experiments getting keras runningtwo options licensed to