Please suggest me which way is best for learn about ANN and ANN modelling for input parameters and output response in machining process. If any one other method you know which better deals to the experimental data compared to ANN than also suggest.
Regards
Janak
One of the best way to get better at ANNs and machine learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. Attached video link is from one of the best machine learning teacher & expert across the globe. It is the complete playlist for the lectures on Stanford Machine Learning (CS229) by Prof. Andrew Ng at Stanford. In these lectures he covers machine learning concepts which include Linear / Logistic regression, supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. He discusses techniques like Naive Bayes, Neural Networks, SVM, Bayesian statistics, Regularization, Clustering, PCA and ICA. He also discusses some recent applications of Machine learning such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. If you are a complete newbie and want a lucid introduction, which challenges you at times, go with these videos
https://www.coursera.org/learn/machine-learning
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599
I am not involved in artificial neural networks, but I checked here and found this link that I hope would be helpful for you: https://www.researchgate.net/search.Search.html?type=publication&query=best%20for%20learn%20about%20ANN%20and%20ANN%20modelling%20for%20input%20parameters%20and%20output%20response%20in%20machining%20process
Best regards,
Debra
One of the best way to get better at ANNs and machine learning is to watch a lecture from an expert and work your way along with it. If you do so, you get the best of both the worlds – you learn from the experts across the globe and also get hands on knowledge. Attached video link is from one of the best machine learning teacher & expert across the globe. It is the complete playlist for the lectures on Stanford Machine Learning (CS229) by Prof. Andrew Ng at Stanford. In these lectures he covers machine learning concepts which include Linear / Logistic regression, supervised and unsupervised learning, learning theory, reinforcement learning and adaptive control. He discusses techniques like Naive Bayes, Neural Networks, SVM, Bayesian statistics, Regularization, Clustering, PCA and ICA. He also discusses some recent applications of Machine learning such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. If you are a complete newbie and want a lucid introduction, which challenges you at times, go with these videos
https://www.coursera.org/learn/machine-learning
https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599
Thankyou very much Mahmoud Omid sir and Debra maam for your valuable suggestions.
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