Foreword
If you are reading that, it's safe to assume that you are well-aware of the tremendous
impact of Artificial Intelligence (AI) and Machine Learning (ML), and the uncanny
effectiveness of deep neural networks. Matei Zaharia and his team started Spark, not as a
competitor to Hadoop, but rather as an effort towards the democratization of AI and ML.
As Zaharia has famously said, The only focus in Spark is how you compute stuff, not where you
store it. Dubbed as unified analytics engine for large-scale data processing, Spark is
optimized for resilience, speed, ease of use, generality, and run-everywhere features, and
this book does a phenomenal job explaining it to you, converting you to a spark enthusiast.
As a reader, if you are excited about getting started with Spark's application in deep
learning, this book can help. The authors begin by helping to set up Spark for Deep
Learning development by providing clear and concisely written recipes. The initial setup is
naturally followed by creating a neural network, elaborating on the pain points of
convolutional neural networks, and recurrent neural networks. AI find new use cases every
day, mostly starting with verticals. In the practitioner's spirit, authors provided practical
(yet simplified) use cases of predicting fire department calls with SparkML, real estate
value prediction using XGBoost, predicting the stock market cost of Apple with LSTM, and
creating a movie recommendation engine with Keras.
The AI and ML landscape is nothing if not heterogeneous; the dizzying diversity of toolset
and libraries can be intimidating. The authors do an excellent job mixing it up with
different libraries, and incorporating relevant yet diverse technologies as the reader moves
forward in the book. As deep learning frameworks start to converge and move up in
abstraction, the scale of the exploratory data analysis inevitably grows. That's why instead
of creating a proverbial one-trick pony (or YOLO model, pun intended), the book covers
pertinent and highly relevant technologies such as LSTMs in Generative Networks, natural
language processing with TF-IDF, face recognition using deep convolutional networks,
creating and visualizing word vectors using Word2Vec and image classification with
TensorFlow on Spark. Aside from crisp and focused writing, this wide array of highly
relevant ML and deep learning topics give the book its core strength.
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