Neo4j’s Post

There are two broad categories of neural network quantization techniques: Post-Training Quantization (PTQ), and Quantization Aware Training (QAT). In QAT, you let the model live in quantised space and train it, letting the gradients and optimizer fine tune your model for the quantised space. I have a new coding tutorial on how to do QAT! I also go over the basics of PyTorch data loaders and training loops, so it’s really a beginner friendly tutorial. Check it out! Quantization Aware Training (QAT) With a Custom DataLoader: Beginner's Tutorial to Training Loops https://lnkd.in/eficVtPd

Blog 3/n… In this blog post, I have discussed about graph representational techniques like Node2Vec, DeepWalk, Graph2Vec etc. They are a crucial part of graph learning algorithms and are fundamentals for them. Do give it a read… #graphneuralnetworks #graphrepresentationallearning #artificialintelligence

However, this kind of direct similarity measurement ignores the local geometry structure of the intrinsic data manifold, which is not discriminative enough for robust image retrieval. Figure 1. The pipeline of the proposed method. The pipeline consists of two stages: the self-supervised training example selection stage and the network fine-tuning stage. In the training example selection part, database images are firstly fed into the CNN model to extract MAC feature representations. The similarity relationship between images is generated from MAC features and refined by manifold learning. Finally, top-ranked image pairs are selected to supervise network fine-tuning. The above two stages can be conducted iteratively until convergence.

Day5:Today I delved into another popular ML algorithm -NaiveBayes classifier which comes under the classification category of supervised Machine learning 🔻 The Math behind this is: Bayes theorem in statistics This algorithm is popularly used for classification and the top use cases of this face /object recognition, News categorization, Spam filtering etc.., Types: 🔻 GAUSSIAN-used in cases of datasets having normal distribution 🔻 MULTINOMIAL-used when the data points are discrete 🔻 BERNOULLI -Used when feature vectors are binary Here,i have learnt the implementation of naive Bayes Classifier using the most popular TITANIC survival dataset .I have used the GaussianNB from the scikitlearn library #MLChallengeday5#LearningBydoing#MachineLearning#MLalgo

Beyond the Basics: While SGD and Adam are staples in PyTorch, advanced algorithms like SLSQP and CMA-ES can tackle tough optimization problems. Learn how these gradient-free methods can enhance your models in Benjamin Bodner's latest article. #PyTorch #DataScience

Hi LinkedIn Folks, Level Up Your NLP Game: Word2Vec for Advanced Feature Representation (Part 3) Continuing our dive into NLP superpowers, this post tackles Word2Vec, a powerful technique for representing words as numerical vectors . We've already explored feature extraction methods like TF-IDF, but Word2Vec goes a step further. In this post, we unravel the mysteries of Word2Vec, exploring key aspects such as: 👉 Word2Vec: Decoding the Language Code 👉 CBOW (Continuous Bag-of-Words): The Prediction Game 👉 Skip-gram: Predicting the Neighborhood 👉 Model Architectures 👉 The Benefits of Word2Vec: Supercharging NLP 👉 Implementation of CBOW Model Check out the full post here: https://lnkd.in/gmbcYZZJ #NLP #FeatureEngineering #MachineLearning #AI #Word2Vec!🔥

I've just finished the "𝑲𝒏𝒐𝒘𝒍𝒆𝒅𝒈𝒆 𝑮𝒓𝒂𝒑𝒉𝒔 𝒇𝒐𝒓 𝑹𝑨𝑮" course from DeepLearning.AI. 🎓 It's an interesting overview on how to leverage the power of knowledge graphs in RAG and enhance work with LLMs. 💡 The course covered a wide range of topics, including: ➡ Understanding the fundamentals of knowledge graphs and how they store data using nodes and edges 📊 ➡ Querying knowledge graphs using Neo4j's Cypher query language 🔍 Preparing unstructured text data for integration with knowledge graphs 📝 ➡ Constructing knowledge graphs from scratch using real-world financial and investment documents 🏗️ ➡ Writing advanced Cypher queries to retrieve relevant information and format it for LLM prompts 📜 here's the link to the course: https://lnkd.in/dx-8GWyj #KnowledgeGraphs #RAG #DeepLearningAI #ArtificialIntelligence #DataScience #Innovation #LLM #Neo4j #Cypher #RAG

Debugging the input flow in a neural network is essential for designing highly performant models both in task metrics and inference. To do so, easy-to-use and features rich tools are required. Here, I share a nice package I've discovered and been using to visual PyTorch models (Keras has a built-in summary() method since ages). As described in the README section: "TorchLens is a package for doing exactly two things: - Easily extracting the activations from every single intermediate operation in a PyTorch model—no modifications needed—in one line of code. "Every operation" means every operation; "one line" means one line. - Understanding the model's computational structure via an intuitive automatic visualization and extensive metadata (partial list here) about the network's computational graph." Paper: https://lnkd.in/dgibn28p Github repo: https://lnkd.in/ekY9u5R3

#Qwen2 #Math it here. Qwen2-Math is a series of specialized math language models built upon the Qwen2 LLMs, which significantly outperforms the mathematical capabilities of open-source models and even closed-source models (e.g., GPT-4o) https://lnkd.in/g_z-uEqR

*Day 4 Boot Camp On Machine Learning* Targets: 1.What Does Classification Mean? 2.How Does Classification Work? 3.Visualizing Classification 4.A classification model which will use TENSORFLOW and KERAS to predict the digit from handwritten digit using the MNIST dataset. #bootcamp #classification_model