Can I implement deep learning models in my laptop with intel hd graphics

You can install and use keras without cuda, but you can't get gpu accelerating with intel hd graphics.

If you use Theano as keras' backend, first install Theano:

# for python2
pip install theano
# for python3
pip3 install theano

Then set ~/.theanorc file like this:

[global]
floatX = float32
device = cpu
allow_gc = True

[blas]
ldflags = -lopenblas

If you use TensorFlow as keras' backend, just install the CPU version of TensorFlow.

# for python2.7
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0-cp27-none-linux_x86_64.whl
# for python3.4
pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0-cp34-cp34m-linux_x86_64.whl
# for python3.5
pip3 install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.12.0-cp35-cp35m-linux_x86_64.whl

Then install keras:

# for python2
pip install keras
# for python3
pip3 install keras

Edit: As of now, you can directly use OpenCL based clDNN (https://github.com/01org/clDNN) instead of using OpenVX, in order to perform Deep Learning inference on Intel Graphics. You will have to do the training on a powerful GPU like Nvidia or AMD and use the pre-trained model and use it in clDNN.

You can start using Intel's Computer Vision SDK (https://software.intel.com/en-us/computer-vision-sdk) in order to write Deep Learning Applications using OpenCV or OpenVX.

OpenVX (https://www.khronos.org/openvx/) programming model allows you to write simple Neural Network pipelines using the following SPEC (https://www.khronos.org/registry/OpenVX/extensions/neural_network/html/)

Alternatively you can use Model Optimizer that converts Caffe/TensorFlow model into OpenVX, and you can accelerate the OpenVX Neural Network graph on Intel Integrated HD Graphics.

Hope it helps.