There are a couple of emerging transformers models designed for predicting time series values like the Informer and the Temporal Fusion Transformer. What are your thoughts on this topic? Do you think they can stand to RNNs? submitted by /u/MrGolran [visit reddit] [comments ..read more

Currently make an educated guess on depth and learning rate, add several white noise variables, and remove all that are lower on the feature importance plot. Is there a better way? I’ve seen other people mention to reduce the tree depth to 1 before removing features below white noise. Another approach is to use the shap values and also to look at pdp plots. submitted by /u/fuzzy_plums [visit reddit] [comments ..read more

Hello to all, i have a mechanical engineering degree and would like to enter in the world of the machine learning and data science. Let’s assume i already have most of the knowledge from calculus, linear algebra and basic python. How much time do you think it can take to land a job as a junior machine learning engineer? My plan is to start multiple online courses like coursera and similar and then build a small portfolio based on kaggle projects. What do you think? Can 1 year be enough? Since i have a full time job i can put up to 10 hours a week to study submitted by /u/Wepp- [visit reddit ..read more

I am sorry for this question but I could not solve this error anyway. here is error code= ---> 60 model.fit((train_features), train_labels) 73 def fit(self, features, labels): ---> 74 self.tree = self.build_tree(features, labels) ---> 53 best_feature, best_threshold = self.find_best_split(features, labels) ---> 24 left_labels = labels[left_mask] I got this error in line 24= TypeError: only integer scalar arrays can be converted to a scalar index. I got image like this: image = cv2.imread(input_path) image = cv2.resize(image, (width, height)) imageCannyEdge = canny_edge_detection(i ..read more

submitted by /u/Jean-Porte [visit reddit] [comments ..read more

I recently read this perspective discussing that the transformer could be replaced. https://towardsdatascience.com/a-requiem-for-the-transformer-297e6f14e189 In general, articles have been published in the past few months showing the limitations of the transformer: https://arxiv.org/abs/2203.15556 https://arxiv.org/abs/2304.15004 in computer vision, ConvNets with the same budget would seem to have similar performance: https://arxiv.org/abs/2310.19909 https://arxiv.org/abs/2310.16764 DeepMind shows that the transformer would not be able to generalize beyond the training set distribution: https ..read more

Structured learning resources work best for most of us, and you might be interested in how ChatGPT really works at the lowest level, and how to code it up from scratch. Unfortunately, a lot of AI/ML resources have way too much math, and are either hella confusing, or just drag on with tedious math proofs. I created the YouTube channel GPT and Chill to focus on what you actually need to know for ML side projects or to break into ML engineering. So far we've covered many ML and neural network concepts from scratch, and the next video in the playlist/series will be on self-attention and coding u ..read more

For a model training on a loss function consisting of weighted losses: ​ https://preview.redd.it/j04h4v0sab4c1.png?width=153&format=png&auto=webp&s=21677d2520375d500b904bb3ae30d403c9941d7c I want to know what can be said about a model that converges based on this ℒ loss in terms of the losses ℒ_i, or perhaps the models that converge on the ℒ_i losses seperately.For instance, if I have some guarantees / properties for models m_i that converge to losses ℒ_i, if some of those guarantees properties transition over to the model m that converges on ℒ. Would greatly appreciate links ..read more

https://twitter.com/rebooted101_py/status/1731671501378605263?t=xZNUl8nPHSQoHcz6j58asQ&s=19 submitted by /u/palashsharma15 [visit reddit] [comments ..read more

I was thinking about LLM agents recently and I've had 2 ideas that felt so natural to me, even though I've not seen many discussions about them. Therefore, I decided to share them with you. First of all, an important idea that I've seen some people pointing out a couple of times is that the LLM output should not be faced as its "voice", but rather its "thoughts". The verbosity of ChatGPT is there for a reason, specially when working with math or code. We should not try to make LLMs less verbose, because that would mean making them "think less" about their responses. Actually, the more verbose ..read more