Fundamentals of Machine Learning Chapter Summary

Fundamentals of Machine Learning Chapter Summary

The book, “Deep Learning with Python” by François Chollet provides an introduction to the field of deep learning with the use of python language as well as the robust Keras library. Deep learning is presented as a form of artificial intelligence grounded on multiple layered neural networks. The book is organized into two parts with about nine chapters, in Chapters 8 and 9, which are “Generative deep learning” and “Conclusion,” which are one of the fascinating topics. Chollet provides a basic understanding of deep generative learning along with a conclusion to the ideas of machine learning.

Chapter 8 focuses on the next generation with the use of LSTM and implementation of the “DeepDream.” Also, performing the transference of the neural style, the variational autoencoders, and a deep understanding of the functionality of the generative adversarial networks. On the idea of how the recurrent neural networks are utilized in the generation of data that is sequential, Chollet (2018) holds that it is not constrained to the artistic generation of content. It has been effectively utilized in the synthesis of the speech as well as dialoguing the production for chatbots. Chollet (2018) adds that the recurrent neural networks are much applicable in the generation of music, dialogue, image, synthesis of the speech, as well as designs of the molecule. A training network produces the sequence data for the prediction of the other tokens along a sequence based on the former token working as the input.

In the generation of the sequence data, Chollet (2018) holds that in the generation of sequence data, it is critical to have a sampling strategy which helps in the choice of the next character. Further, for the implementation of the LSTM on the level of the character, it demands the preparation of data, the creation of the network, training of the language model and IT, sampling. Then, one needs to carry out a wrapping up for the prediction of the next tokens (Chollet, 2018).   DeepDream is a crucial technique that is utilized in modifying image and adopts the representations observed through the convoluted neural networks. This involves the implementation of DeepDream in Keras and wrapping up.

The other vital aspects are the transfer of the neural style, which involves the application of the style of an image of reference to a given image with the conservation of content provided in the target image. Some issues to consider in the transfer is the possible content loss, the style of loss, and their transfer in the case of Keras (Chollet, 2018).  Finally, this chapter shows how images are generated with different autoencoders. Also, taking into account the latent image spaces and content vectors and conclude with the generative adversarial networks. Notably, it is grounded on the generator and discriminator network.

In Chapter 9, the conclusion, Chollet (2018) provides an overview of the book, including the key takeaways, the constraint associated with deep learning, and the prospects and finally the resource that can be utilized for the further learning. Some of the critical shortcomings of deep learning include the threat associated with anthropomorphization of the models used in machine learning, the collision between the local and extreme generalization, and wrapping. In the future, the author predicts the models will function as a program of the computer, and newer forms of learning will emerge. Also, there will be new models that do not require human involvement in engineering and the increased use of learning systems.

Conclusion

In conclusion, the two chapters are instrumental in the process of understanding the concept of deep learning with python. The chapters are apparent and well connected with other chapters making it easier to relate them. The chapter reviews focused on the “Generative deep learning” and “Conclusion,” in which Collet relates them intending to further the comprehension of the ideas that can be used for future advancement.

References

Chollet, F. (2018).  Fundamentals of machine learning. In Deep Learning with Python MITP-Verlags GmbH & Co. KG.