Audible Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play –

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play Generative Modeling Is One Of The Hottest Topics In AI It S Now Possible To Teach A Machine To Excel At Human Endeavors Such As Painting, Writing, And Composing Music With This Practical Book, Machine Learning Engineers And Data Scientists Will Discover How To Re Create Some Of The Most Impressive Examples Of Generative Deep Learning Models, Such As Variational Autoencoders,generative Adversarial Networks GANs , Encoder Decoder Models And World Models Author David Foster Demonstrates The Inner Workings Of Each Technique, Starting With The Basics Of Deep Learning Before Advancing To Some Of The Most Cutting Edge Algorithms In The Field Through Tips And Tricks, You Ll Understand How To Make Your Models Learn Efficiently And Become Creative Discover How Variational Autoencoders Can Change Facial Expressions In Photos Build Practical GAN Examples From Scratch, Including CycleGAN For Style Transfer And MuseGAN For Music Generation Create Recurrent Generative Models For Text Generation And Learn How To Improve The Models Using Attention Understand How Generative Models Can Help Agents To Accomplish Tasks Within A Reinforcement Learning Setting Explore The Architecture Of The Transformer BERT, GPT And Image Generation Models Such As ProGAN And StyleGAN El libro lleg con suciedad y polvo. Bien The book starts great Fantastic examples It appeals to the reader s intuition and imagination I loved the beginning and it was very easy working side by side with Jupyter Notebook The examples are easy to follow and the code is pure Python with Keras At that point I was going to give the book five stars However, I was stuck at Autoencoders when the author suddenly started using his own code shortcuts, which was completely unexpected It took me a while to figure it out that the code was no longer Keras but the functions and objects developed by the author, and imported from the local python files The book does not explain any of this and the code becomes very obscure The author s models and utilities were clearly meant to simplify development of complex neural networks by the reader Unfortunately, the code is no longer intelligible as it hides the true Keras APIs These shortcuts are not really necessary and the code they replace would not add much to the size of the book In those circumstances, if you move away from the book and the author s Github repository, you will no longer be able to reproduce the models and their tests easily While it is expected of any practitioner to develop his or her own helper library, this is not suitable for the book which needs simplicity and clarity In all honesty, the book does not claim to train the reader in Keras at all, however, it uses Keras and asks the reader to install the software, and then explains the basics of model creation with Keras, only to leave it behind I d recommend to replace all obscure code with the simplest model creation, which can be found in any Keras example on the web As the author is quite responsive to the reviews and open for comments, I have increased my rating.

Leave a Reply

Your email address will not be published. Required fields are marked *