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Understanding Computation Graphs in Pytorch vs Tensorflow

Understanding Computation Graphs Understanding Computation Graphs in Deep Learning Part 1: Theoretical Explanation What Is a Computation Graph? A computation graph is a core concept in deep learning that defines the flow of data and operations within a model. It is a directed acyclic graph (DAG) where: Nodes: Represent operations (e.g., addition, multiplication, activation functions). Edges:…

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Compare iPad M4 chip, Mac M4 Chip , Cloud & GoogleColab for building deep learning model - day 10

Aspect Details Considerations CPU and GPU Multi-core CPU, 10-core GPU Efficient for basic processing and on-device inference but limited for intensive deep learning tasks. Neural Engine 16-core Neural Engine Optimized for running pre-trained Core ML models efficiently, but limited utility for training tasks. Memory Configuration 16GB unified memory (on 1TB model) Adequate for small to…

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Solo Developer’s Guide to Building Competitive Language Model Application - day 9

Solo Developer’s Guide to Building Competitive Language Model Applications A Solo Developer’s Guide to Building Competitive Language Model Applications With the explosion of large language models (LLMs) like ChatGPT, more developers than ever are exploring how to create their own language model-based applications. While large tech companies have an advantage with resources and infrastructure,…

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Where to Get Data for Machine Learning and Deep Learning Model Creation - day 8

Where to Get Data for Machine Learning and Deep Learning Model Creation Where to Get Data for Machine Learning and Deep Learning Model Creation 1. Primary Data Sources for Machine Learning and Deep Learning Source Description Kaggle A community-driven platform offering a variety of datasets, including image, text, and structured data. UCI Machine Learning Repository A longstanding repository…

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GPU and Computing Technology Comparison 2024 - day 7

GPU and Computing Technology Comparison 2024 Exploring the Latest in GPU and Computing Technology: NVIDIA, Google Colab, Apple M4 Chip, and More In the rapidly evolving world of technology, GPUs (Graphics Processing Units) have become pivotal, extending far beyond their initial purpose of rendering graphics. Today, GPUs are foundational in fields such as machine learning, data analysis,…

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Fine-Tuning vs. Transfer Learning in Voice Synthesis

Fine-Tuning vs. Transfer Learning in Voice Synthesis Fine-Tuning vs. Transfer Learning: Adapting Voice Synthesis Models Introduction In the world of deep learning, transfer learning and fine-tuning are essential techniques that enable pre-trained models to quickly adapt to new tasks. These techniques are especially valuable in complex tasks like voice synthesis, where the model must capture the…

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Fine-Tuning in Deep Learning with a practical example - day 6

Understanding Fine-Tuning in Deep Learning Understanding Fine-Tuning in Deep Learning: A Comprehensive Overview Fine-tuning in deep learning has become a powerful technique, allowing developers to adapt pre-trained models to specific tasks without training from scratch. This approach is especially valuable in areas like natural language processing, computer vision, and voice cloning. In this…

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DeepNet – Scaling Transformers to 1,000 Layers new for 2024 - day 5

DeepNet – Scaling Transformers to 1,000 Layers DeepNet – Scaling Transformers to 1,000 Layers: The Next Frontier in Deep Learning Introduction In recent years, Transformers have become the backbone of state-of-the-art models in both NLP and computer vision, powering systems like BERT, GPT, and LLaMA. However, as these models grow deeper, stability becomes a significant hurdle. Traditional…

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Transformers seems most famous for deep learning on 2024 so lets learn it more - day 4

Transformers in Deep Learning (2024): Types, Advances, and Mathematical Foundations Transformers have transformed the landscape of deep learning, becoming a fundamental architecture for tasks in natural language processing (NLP), computer vision, and beyond. Since their inception in the 2017 paper “Attention is All You Need” by Vaswani et al., transformer architectures have continuously evolved,…

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You wanna use ChatGPT 4o by tokens instead o buying monthly subscription but you do not know how to implement on Mac Xcode ? Here we discuss how to Implement chatgpt API on Xcode On Mac on 2024 : (day3)

Introduction: Choosing Between ChatGPT Plus and Token-Based API for SwiftUI Integration When integrating OpenAI’s ChatGPT into your SwiftUI application, you have two primary options: subscribing to ChatGPT Plus or utilizing the ChatGPT API with token-based pricing. Each approach offers distinct advantages and considerations. ChatGPT Plus Subscription ChatGPT Plus is a subscription service priced…

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New on 2024 - 2025 are MLX and Transofermers so lets compare Custom Deep Learning Models for iOS with MLX on Apple Silicon vs. PyTorch - day 2

Building Custom Deep Learning Models for iOS with MLX on Apple Silicon vs. PyTorch New on 2024 – 2025 are MLX and Transofermers so lets compare Custom Deep Learning Models for iOS with MLX on Apple Silicon vs. PyTorch The development of deep learning applications for iOS has become increasingly sophisticated with Apple’s M-series chips, which allow for powerful local processing on mobile…

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Deep Learning in 2024: Continued Insights and Strategies - day 1

Deep Learning in 2024: Latest Models, Applications, and Monetization Strategies Deep Learning in 2024: Latest Models, Applications, and Monetization Strategies In 2024, deep learning continues to be at the forefront of innovation, influencing industries across various domains. For solo developers and iOS app creators, the year brings an array of models, frameworks, and profitable opportunities.…

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How Dalle Image Generator works ? - day 76

First check our previous article about what is Diffusion Model before reading this article DALL-E Blog Post How DALL-E Works: A Comprehensive Guide to Text-to-Image Generation DALL-E, developed by OpenAI, is a revolutionary model that translates text prompts into detailed images using a complex, layered architecture. The recent 2024 update to DALL-E introduces enhanced capabilities, like…

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Breaking Down Diffusion Models in Deep Learning – Day 75

Unveiling Diffusion Models: From Denoising to Generative Art The field of generative modeling has witnessed remarkable advancements over the past few years, with diffusion models emerging as a powerful class capable of generating high-quality, diverse images and other data types. Rooted in concepts from thermodynamics and stochastic processes, diffusion models have not only matched but, in some…

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Understanding Unsupervised Pretraining Using Stacked Autoencoders

Understanding Unsupervised Pretraining Using Stacked Autoencoders Part 1: Understanding Unsupervised Pretraining Using Stacked Autoencoders Introduction: Tackling Complex Tasks with Limited Labeled Data When dealing with complex supervised tasks but lacking sufficient labeled data, one effective solution is unsupervised pretraining. In this approach, a neural network is first trained to perform…

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Unlock the Secrets of Autoencoders, GANs, and Diffusion Models – Why You Must Know Them? -Day 73

Autoencoders, GANs, and Diffusion Models – A Deep Dive Part 1: Understanding Autoencoders, GANs, and Diffusion Models – A Deep Dive In this post, we’ll explore three key models in machine learning: Autoencoders, GANs (Generative Adversarial Networks), and Diffusion Models. These models, used for unsupervised learning, play a crucial role in tasks such as dimensionality reduction, feature…

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What is NLP and the Math Behind It? - day 71

Understanding NLP and the Math Behind It What is NLP and the Math Behind It? Understanding Transformers and Deep Learning in NLP Introduction to NLP Natural Language Processing (NLP) is a crucial subfield of artificial intelligence (AI) that focuses on enabling machines to process and understand human language. Whether it’s machine translation, chatbots, or text analysis, NLP helps bridge the…