There are some easy and comprehensive textbooks covering many deep learning concepts using PyTorch in detail. But, I am a little dissatisfied with the weightage given to RNNs compared to CNNs. I mean, some textbooks did not cover RNN at all. I am in need of learning NLP using PyTorch to great detail. So, I want the list of books or a single book that covers RNN, LSTM, etc., using PyTorch in great detail i.e., from beginner to advanced. I …

I'm looking for recommendations of books in the area of Infrastructure/SRE/DevOps but oriented to DL/ML. I know a few of them that seem to be too focused on production but what about training? How to design a cluster for deep models training? What about caching data for very large datasets? Is there such a thing? Or maybe a more general oriented book which principles can be applied easily to designing DL systems for training. Thanks in advance!

I was reading Modern Optimization with R (Use R!) and wondering if a book like this exists in Python too? To be precise something that covers stochastic gradient descent and other advanced optimization techniques. Many thanks!

I currently re-read Foundations of Statistical Natural Language Processing and enjoyed it, but as there are some hard exercises and I have doubts about my solutions. I would like to know if there is a solutions manual for this book so that I can check my answers. Is there?

There are many books in the stores on data science. Is there any book that gives complete guideline to data science? Like separately talks about python (not R), statistics, math, data Visualization software etc. Overall, I need to have a book that talks about complete guideline to data science in any field, or important fields like business, commerce etc.

I've read a couple of guides such as this link. I'm having real trouble getting to grips with how to use time series data effectively. Anything to help with the intuition ideally with some case studies. I am currently reading Applied predictive modelling so anything with a similar pedagogy would be ideal.

I've been through a bunch of books, resources and articles and I'm wondering if there are any specific classes/fields that I can audit. There aren't any brewing programs near me in the formal sense so I'm looking for college-level classes or something that would benefit my homebrewing and further a potential career.

I have heard of genetic algorithms, but I have never seen practical examples and I've never got a systematic introduction to them. I am now looking for a textbook which introduces genetic algorithms in detail and gives practical examples how they are used, what their strengths are compared to other solution methods and what their weaknesses are. Is there any standard textbook for this?

Over the past two years, I have been working as a full-time data scientist for a government company. As the sole data science team in the organization, our job is a hybrid between data science and machine learning engineering. We need to research and develop ml solutions for the organization's business problems as well as implement them in production environments. The problem is I'm feeling stuck knowledge-wise and I don't know what can I do about that. Let me explain. …

Can anybody recommend any books or resources that would be good for better understanding the mathematical notation that is often seen in ML/DL papers? Preferably with examples of how the notation can be "transcribed" into tensorflow code. I did Linear Algebra and Vector calculus in uni a long time ago but it is a long way back and I would like to get back up to speed with it. This question I think is not what I am looking for …

I would like to ask what are the current recommendations for professional and comprehensive, current books on machine learning. The two quite classic comprehensive text-books are: Machine Learning: A Probabilistic Perspective (Murphy, 2012) The Elements of Statistical Learning: Data Mining, Inference, and Prediction (By Springer Statistics, 2009) But they are somewhat dated, so I wonder if there are more recent choices? You may also recommend books that focus on sub-sections of ML, or anything that is recent and of note.

In the present, Deep Learning is experiencing lightspeed growth, with plethora of new architectures and radiant ideas emerging each month. Since the last few years several influential ideas, applied in different papers have been developed - among which most important are Transformer, EfficientNet, MobileNets. Is there some modern reference, in the spirit of the famous https://www.deeplearningbook.org/, in some way, generalizing the most prominent advances in Deep Learning?

I have been trying to understand reinforcement learning for quite sometime, but somehow I am not able to visualize how to write a program for reinforcement learning to solve a grid world problem. Can you suggest me some text books which would help me build a clear conception of Reinforcement Learning?

I have found other requests for references here. In particular in: Where to start, which books and Books about the "Science" in Data Science? I have given a glance to: Artificial Intelligence: A Modern Approach (Russel & Norvig) Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach) Learning From Data (Abu-Mostafa et al.) Introduction to Statistical Learning (James et al.) Elements of Statistical Learning (Hastie et al.) Pattern Recognition and Machine Learning (Bishop) Now it …

Considering, I do have some knowledge about machine learning, python, data science through the courses I have done from sites like Udemy, Udacity, Edx. However, I would like to read some books related to the subject matter. Could you please refer me to some helpful books related to machine learning, python.

Can people provide the best textbook they have used for learning data science? I know this is somewhat difficult because you have no basis of comparison but if you can just give your opinion on a book and why, I'd greatly appreciate it.

Is it possible to perform Book index searching using Machine learning algorithms? Inputs : 1 Book pages with page numbers as images. 2 Index words in the book. Output: Tracing the page number/s with the indexes provided.

I have seen that people coming into data science will rush into scikit-learn or other libraries without trying to learn the knowledge behind. Its good to follow a top-down approach but most of times people don't go into depth, even if they do its in supervised-learning area. That't why I think un-supervised is neglected (or is it heavy influence of statistical inference). I would like to learn the un-supervised learning in-depth I tried some books so far (Sugiyama, Masashi - …

I'm reading the book Fundamentals of Machine Learning for Predictive Data Analytics by Kelleher, et al. I've come across something that I think as an error but I want to check to be sure. When explaining precision and recall the authors write: Email classification is a good application scenario in which the different information provided by precision and recall is useful. The precision value tells us how likely it is that a genuine ham email could be marked as spam …

After working with predictive maintenance using ML algorithms for a while, I felt like I needed more theoretical background. To do so, I started some courses, but they don't dig into the hole and just explain demo cases. I started searching for books (in reddit mostly), but I feel like the books that I find are not that good. Can someone list top books of predictive maintenance using machine learning?