Acknowledgments

I have drawn, sometimes rather heavily, from the following resources. Thanks to the authors and organizations for sharing their knowledge.

intellectual property

The content of this site is used for educational purposes only. Anyway, if you think it violates your intellectual property rights, reach out to me and we’ll fix that.

Books

• Hands-on Machine Learning with scikit-learn, Keras & TensorFlow (source code) by Aurélien Géron.

• Deep Learning with Python (source code) by François Chollet.

• Data Science From Scratch (source code) by Joel Grus.

• A Whirlwind Tour of Python and Python Data Science Handbook by Jake VanderPlas.

• Neural Networks and Deep Learning by Michael Nielsen.

Courses

• Google’s Machine Learning Crash Course.

• Andrew Ng’s Machine Learning Course and Deep Learning Specialization on Coursera.

• UCLxDeepMind lecture series On Deep Learning.

Repositories

• Homemade Machine Learning by Oleksii Trekhleb.

Articles

• A ‘Brief’ History of Neural Nets and Deep Learning by Andrey Kurenkov.

• Applied Deep Learning (4 parts) by Arden Dertat.

Blogs

• Chris Olah.

• Andrej Karpathy.