welcome

Dear Reader

Thanks for purchasing the MEAP for Deep Learning Design Patterns.

My readers are software engineers, machine learning engineers and junior, mid-level and senior data scientists. For the latter, you might assume that the initial chapters would be redundant; but with my unique approach you will likely find additional insight as a welcomed refresher. The organization of the book is targeted towards job role levels in today’s AI market, and assisting you in progressing upwards. My book is organized into job level parts: novice (covered in the free online Primer), junior data scientist (Parts 1),  mid-level data scientist (Part 2) and senior data scientist (Parts 3). The material in each Part covers many of the expected duties to master for that specific level, and prepare you to progress into a higher job level.

For those starting at the junior data scientist level, you should be familiar with Python and have some familiarity with machine learning, such as having taken an online course, learning on the job, or self-taught. You should know basic machine learning concepts behind regression/classification and convolutional neural networks. You should already know how a convolutional layer works, what is batch normalization, convolution block patterns such as VGG, and residual block patterns such as ResNet. For those not familiar with all these concepts, I cover them in my freely distributed Primer available on GitHub.