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Damian Valles Nov 27, 2020

This is a great introduction book by Pablo Rivas to Deep Learning concepts. The book provides great fundamental concepts that are flexible to Sciences and Engineering domains. Also, all proceedings are donated to Latinx in AI! #deeplearning #ai

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Rahul Gupta Feb 19, 2021

“Deep Learning for Beginners” by Dr. Pablo Rivas offers a great beginner's guide to getting up and running with deep learning from scratch using Python. It provides step-by-step guided instructions with code and examples explaining basic concepts with practical examples the theory and concepts of Deep Learning in diverse fields such as computer vision, natural language processing, learning representations etc. The book is organized to provide gradual transition between supervised and unsupervised models. Google Colabs, the free website, has been used to provide deep learning tools and libraries accessible to anyone enabling them to run the code on the cloud.Chapter 1, Introduction to Machine Learning, gives an overview of machine learning and introduces the motivation behind machine learning and the commonly used terminologies. It also introduces deep learning and how it fits in the realm of artificial intelligence.Chapter 2, Setup and Introduction to Deep Learning Frameworks, explains the process of setting up TensorFlow and Keras and their usefulness in deep learning.Chapter 3, Preparing Data, introduces the main concepts behind data processing for Deep Learning. It covers essential concepts of data formatting that are categorical or real-valued, as well as techniques for augmenting data or reducing the data dimensions.Chapter 4, Learning from Data, introduces elementary concepts of the theory of deep learning, including measuring performance on regression and classification as well as the identification of underfitting and overfitting and optimizing hyperparameters.Chapter 5, Training a Single Neuron, introduces the concept of a single neuron and the perceptron model, the key to understanding basic neural models that learn from data and explains the problem of non-linearly separable data.Chapter 6, Training Multiple Layers of Neurons, covers deep learning using the multi-layer perceptron (MLP) algorithm, such as gradient descent techniques for error minimization, and hyperparameter optimization.Chapter 7, Autoencoders, describes the AE model by explaining the necessity of both encoding and decoding layers, the loss functions associated with the autoencoder problem and applies it to the dimensionality reduction problem and data visualization.Chapter 8, Deep Autoencoders, introduces the idea of deep belief networks and the significance of deep unsupervised learning. It explains the concepts by introducing deep AEs and contrasting them with shallow AEs.Chapter 9, Variational Autoencoders, covers the principles behind generative models in the unsupervised deep learning field and their importance in the production of robust models free from noise and demonstrates as to why the VAE is a better alternative to a deep AE when working with perturbed data.Chapter 10, Restricted Boltzmann Machines, covers deep belief models by presenting RBMs. The backward-forward nature of RBMs is introduced and contrasted with the forward-only nature of AEs. The chapter compares RBMs and AEs on the problem of data dimensionality reduction using visual representations of the reduced data.Chapter 11, Deep and Wide Neural Networks, explains the difference in performance and complexities of deep versus wide neural networks and introduces the concept of dense networks and sparse networks in terms of the connections between neurons.Chapter 12, Convolutional Neural Networks, introduces CNNs, starting with the convolution operation and moving towards ensemble layers of convolutional operations aiming to learn and apply data filters how to visualize the learned filters.Chapter 13, Recurrent Neural Networks, presents the concepts of recurrent networks, exposing their shortcomings to justify the existence and success of long short-term memory (LSTM) models. Sequential models are explored with applications for image processing and natural language processing.

AT Mar 24, 2021

Disclaimer: The publisher asked me to review this book & gave me a review a copy. I promise to give my honest opinion about this book.This book covers all fundamentals of ML ecosystem with hands on example codes. It guides you to set up different ML frameworks. You will understand the concepts of data cleansing & preprocessing, basic ML algorithms, tuning hyper parameters, model evaluation. It covers great details on neural network architectures like CNN, RNN, AE, VAE, and GANs. After reading this, you will have pretty good idea about ML concepts and should be able to train some ML model by yourself.

Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python

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Deep Learning for Beginners: A beginner's guide to getting up and running with deep learning from scratch using Python

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