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http://www.aparat.com/v/0scM5 Irene Chen A Beginner's Guide to Deep Learning. What is #Deep_Learning ? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to #machine_learning.

http://www.aparat.com/v/Corus Advanced users #Deep_Learning, anyone who has followed #machine_learning over the past years has heard it. In this talk I will go past the hype and show what deep learning actually means and how one goes about solving complex machine learning task with a minimum amount of code, with the help of theano, an amazing python library for deep learning.

https://www.analyticsvidhya.com/blog/2016/08/deep-learning-path/?utm_content=bufferd56c5&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer #Deep_Learning, a prominent topic in #Artificial_Intelligence domain, has been in the spotlight for quite some time now. It is especially known for its breakthroughs in fields like Computer Vision and Game playing (Alpha GO), surpassing human ability. Since the last survey, there has been a drastic increase in the trends. (click here to check out the survey) Here is what Google trends shows us:

https://github.com/BVLC/caffe #Caffe is a #deep_learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

http://www.kdnuggets.com/2017/09/essential-data-science-machine-learning-deep-learning-cheat-sheets.html#.WdGzWthHcEo.linkedin 30 Essential #Data_Science , #machine_learning & #Deep_Learning Cheat Sheets

#jupyter_notebook#deep_learning#pytorch You can learn PyTorch effectively in 20 days with a friendly, well-structured guide designed for those who already know some machine learning basics and have used Keras, TensorFlow, or PyTorch before. The book breaks down PyTorch concepts from easy to hard, with clear examples and practical code you can use right away. It includes a daily plan requiring 30 minutes to 2 hours, covering modeling, core concepts, APIs, and even advanced topics like GPU training and recommendation systems. This approach makes mastering PyTorch easier and faster, helping you build strong skills for deep learning projects and real applications. https://github.com/lyhue1991/eat_pytorch_in_20_days

https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/learning-path-data-science-python/ Comprehensive learning path – #Data_Science in Python Journey from a Python noob to a Kaggler on Python So, you want to become a data scientist or may be you are already one and want to expand your tool repository. You have landed at the right place. The aim of this page is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of steps you need to learn to use Python for #data_analysis. If you already have some background, or don’t need all the components, feel free to adapt your own paths and let us know how you made changes in the path. You can also check the mini version of this learning path #Deep_Learning

#python#brain_inspired_ai#deep_learning#large_language_models#reasoning The Hierarchical Reasoning Model (HRM) is a new type of AI that reasons more like a human brain, using a fast part for quick details and a slow part for big-picture planning. It solves hard logic tasks like Sudoku, mazes, and IQ-style puzzles very well, even though it is tiny (only 27 million parameters) and learns from very little data (just 1,000 examples). Unlike most large language models, it does not need long chains of written reasoning steps or huge amounts of training, which makes it much faster, cheaper, and more efficient. For the user, this means powerful reasoning in a small, fast system that can run on ordinary hardware and still beat much larger models on tough problems. https://github.com/sapientinc/HRM

Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras Time series prediction problems are a difficult type of predictive modeling problem. Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of #neural_network designed to handle #sequence dependence is called #recurrent_neural_networks . The Long Short-Term Memory network or LSTM network is a type of recurrent neural network used in #deep_learning because very large architectures can be successfully trained. https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/

#python#deep_learning#inference#llm#nlp#pytorch#transformer Nano-vLLM is a small, fast, and easy-to-understand tool for running large language models offline. It matches the speed of bigger systems like vLLM but uses only about 1,200 lines of clean Python code, making it simple to read and modify. It includes smart features like prefix caching and tensor parallelism to boost performance. You can install it easily and run models like Qwen3-0.6B on your own GPU. This tool is great if you want fast, efficient AI inference without complex setups, ideal for learning, research, or small deployments on limited hardware. https://github.com/GeeeekExplorer/nano-vllm

#python#deep_learning#intel#machine_learning#neural_network#pytorch#quantization Intel Extension for PyTorch boosts the speed of PyTorch on Intel hardware, including both CPUs and GPUs, by using special features like AVX-512, AMX, and XMX for faster calculations[5][2][4]. It supports many popular large language models (LLMs) such as Llama, Qwen, Phi, and DeepSeek, offering optimizations for different data types and easy GPU acceleration. This means you can run advanced AI models much faster and more efficiently on your Intel computer, with simple setup and support for both ready-made and custom models. https://github.com/intel/intel-extension-for-pytorch

http://codeinpython.com/tutorials/deep-learning-tensorflow-keras-pytorch/?nonamp=1 Deep Learning #Tensorflow vs #Keras vs #PyTorch #Deep_learning is the application of artificial #neural_networks (ANNs) to learn tasks. These tasks contain more than one hidden layer. Deep learning is part of a broader family of #machine_learning. Machine learning itself is a part of #Artificial_Intelligence(#AI).