Planning to do some deep learning course sessions e.g. fastai , google ML crash course etc. Also try to understand common tools (tensorflow, jupyter notebook, numpy, pandas, pytorch) and practices e.g. Convoluation neutral nets, SGD used to solve learning problems.

Aim is to get ready for kaggle competition (https://www.kaggle.com/competitions) eventually to test out learning and develop intuition around categories of learning problems.

http://course.fast.ai/ ( useful links for setting us GPU server env in AWS for this course ..

http://forums.fast.ai/t/aws-ami-available-for-testing/7255

https://towardsdatascience.com/setting-up-and-using-jupyter-notebooks-on-aws-61a9648db6c5

https://developers.google.com/machine-learning/crash-course/

Intro to Pandas https://colab.research.google.com/notebooks/mlcc/intro_to_pandas.ipynb?utm_source=mlcc&utm_campaign=colab-external&utm_medium=referral&utm_content=pandas-colab&hl=en

TensorFlow: Install Tensorflow https://www.tensorflow.org/install Download models https://github.com/tensorflow/models.git TensorFlow tutorial: https://www.youtube.com/watch?v=yX8KuPZCAMo