PyTorch vs Tensorflow: Who Wins in CNN?

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

35:19

We’ve turned intuition into a buzzword—flattened it into a slogan, a gut feeling, or a vague whisper we don’t always know how to hear. But what if intuition is so much more? What if it's one of the most powerful tools we have—and we’ve just forgotten how to use it? In this episode, I’m joined by Hrund Gunnsteinsdóttir , Icelandic thought leader, filmmaker, and author of InnSæi: Icelandic Wisdom for Turbulent Times . Hrund has spent over 20 years studying and teaching the science and art of intuition through her TED Talk, Netflix documentary ( InnSæi: The Power of Intuition ), and global work on leadership, innovation, and inner knowing. Together, we explore what intuition really is (hint: not woo-woo), how to cultivate it in a culture obsessed with logic and overthinking, and why your ability to listen to yourself might be the most essential skill you can develop. In This Episode, We Cover: ✅ Why we’ve misunderstood intuition—and how to reclaim it ✅ Practical ways to strengthen your intuitive muscle ✅ What Icelandic wisdom teaches us about inner knowing ✅ How to use intuition during uncertainty and decision-making ✅ Why trusting yourself is an act of rebellion (and power) Intuition isn’t magic—it’s a deep, internal guidance system that already exists inside you. The question is: are you listening? Connect with Hrund: Website: www.hrundgunnsteinsdottir.com TedTalk: https://www.ted.com/talks/hrund_gunnsteinsdottir_listen_to_your_intuition_it_can_help_you_navigate_the_future?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare Newsletter: https://hrundgunnsteinsdottir.com/blog/ LI: www.linkedin.com/in/hrundgunnsteinsdottir IG: https://www.instagram.com/hrundgunnsteinsdottir/ Book: InnSæi: Icelandic Wisdom for Turbulent Times Related Podcast Episodes: How To Breathe: Breathwork, Intuition and Flow State with Francesca Sipma | 267 VI4P - Know Who You Are (Chapter 4) Gentleness: Cultivating Compassion for Yourself and Others with Courtney Carver | 282 Share the Love: If you found this episode insightful, please share it with a friend, tag us on social media, and leave a review on your favorite podcast platform! 🔗 Subscribe & Review: Apple Podcasts | Spotify | Amazon Music…

about a year ago 11:49

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This research paper examines the efficiency of two popular deep learning libraries, TensorFlow and PyTorch, in developing convolutional neural networks. The authors aim to determine if the choice of library impacts the overall performance of the system during training and design. They evaluate both libraries using six criteria: user-friendliness, available documentation, ease of integration, overall training time, overall accuracy, and execution time during evaluation. The paper proposes a novel methodology for comparing these libraries by eliminating external factors that could influence the comparison and focusing solely on the six chosen criteria. The study finds that while both libraries offer similar capabilities, PyTorch is better suited for tasks that prioritize speed and ease of use, while TensorFlow excels in tasks demanding accuracy and flexibility. The authors conclude that the choice of library has a significant impact on both design and performance and that the presented criteria can assist users in selecting the most appropriate library for their specific needs.

71 episodes

PyTorch vs Tensorflow: Who Wins in CNN?

published about a year ago

MP3•Episode home

Archived series ("Inactive feed" status)

When? This feed was archived on May 02, 2025 14:13 (18d ago). Last successful fetch was on November 09, 2024 13:09 (6M ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

71 episodes

All episodes

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Automating Scientific Discovery: ScienceAgentBench 9:49

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Introducing, ScienceAgentBench, a new benchmark for evaluating language agents designed to automate scientific discovery. The benchmark comprises 102 tasks extracted from 44 peer-reviewed publications across four disciplines, encompassing essential tasks in a data-driven scientific workflow such as model development, data analysis, and visualization. To ensure scientific authenticity and real-world relevance, the tasks were validated by nine subject matter experts. The paper presents an array of evaluation metrics for assessing program execution, results, and costs, including a rubric-based approach for fine-grained evaluation. Through comprehensive experiments on five LLMs and three frameworks, the study found that the best-performing agent, Claude-3.5-Sonnet with self-debug, could only solve 34.3% of the tasks using expert-provided knowledge. These findings highlight the limitations of current language agents in fully automating scientific discovery, emphasizing the need for more rigorous assessment and future research on improving their capabilities for data processing and utilizing expert knowledge. Read the paper: https://arxiv.org/pdf/2410.05080…

1
Prune This! PyTorch and Efficient AI 8:04

28 weeks ago8:04

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Predicting the Future from the Past: Sequential RNN Stuff 9:47

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This text is an excerpt from the "Dive into Deep Learning" book, specifically focusing on the processing of sequential data. The authors introduce the challenges of working with data that occurs in a specific order, like time series or text, and how these sequences cannot be treated as independent observations. They delve into autoregressive models, where future values are predicted based on past values, and highlight the common problem of error accumulation when predicting further into the future. The text discusses the concept of Markov models, where only a limited history is needed to predict future events, as well as the importance of understanding the causal structure of the data. The excerpt then provides a practical example of using linear regression for autoregressive modeling on synthetic time series data and demonstrates the limitations of simple models for long-term prediction. Read more: https://d2l.ai/chapter_recurrent-neural-networks/sequence.html…

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LLM Tokenizers, from HFs LNP Course 12:23

29 weeks ago12:23

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This excerpt from Hugging Face's NLP course provides a comprehensive overview of tokenization techniques used in natural language processing. Tokenizers are essential tools for transforming raw text into numerical data that machine learning models can understand. The text explores various tokenization methods, including word-based, character-based, and subword tokenization, highlighting their advantages and disadvantages. It then focuses on the encoding process, where text is first split into tokens and then converted to input IDs. Finally, the text demonstrates how to decode input IDs back into human-readable text. Read more: https://huggingface.co/learn/nlp-course/en/chapter2/4…

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PyTorch vs Tensorflow: Who Wins in CNN? 11:49

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29 weeks ago11:57

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The research paper "MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts" explores a novel approach to language modeling by combining State Space Models (SSMs), which offer linear-time inference and strong performance in long-context tasks, with Mixture of Experts (MoE), a technique that scales model parameters while minimizing computational demands. The authors introduce MoE-Mamba, a model that interleaves Mamba, a recent SSM-based model, with MoE layers, resulting in significant performance gains and training efficiency. They demonstrate that MoE-Mamba outperforms both Mamba and standard Transformer-MoE architectures. The paper also explores different design choices for integrating MoE within Mamba, showcasing promising directions for future research in scaling language models beyond tens of billions of parameters. Read it: https://arxiv.org/abs/2401.04081…

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Agentic Retrieval Augmented Generation (RAG) systems 7:39

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Let's Get Activated! Why Non-Linear Activation Matters 7:15

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This video discusses the vanishing gradient problem, a significant challenge in training deep neural networks. The speaker explains how, as a neural network becomes deeper, gradients—measures of how changes in network parameters affect the loss function—can decrease exponentially, leading to a situation where early layers of the network are effectively frozen and unable to learn. This problem arises because common activation functions like the sigmoid function can produce very small derivatives, which compound during backpropagation. The video then explores solutions like using different activation functions (like ReLU) and architectural changes (like residual networks and LSTMs) to mitigate this issue. Watch the video: https://www.youtube.com/watch?v=ncTHBi8a9uA&pp=ygUSdmFuaXNoaW5nIGdyYWRpZW50…

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Automating Scientific Discovery: ScienceAgentBench 7:38

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