Building Blocks of neural networks
Rashid Ul Haq
📘 Just finished Chapter 2 of "Deep Learning with Python" by François Chollet! 🚀
This chapter dives deep into the fascinating world of tensors, matrices, and the foundational building blocks of deep learning. Here are some key takeaways:
🔍 Handwritten Digit Recognition: Starts with a simple neural network example to recognize handwritten digits.
🔢 Core Concepts: Learn about scalars, vectors, matrices, and tensors, and their operations.
📈 Derivatives: Understand how derivatives play a crucial role in optimization.
⚡️ Stochastic Gradient Descent: A powerful technique for training models efficiently.
🔄 Backpropagation Algorithm: The backbone of training neural networks.
🧮 Computation Graphs & Gradient Tape: How TensorFlow uses these for differentiation.
💻 From Scratch in TensorFlow: Step-by-step guide to implementing a neural network from scratch.
Excited to continue this journey and share more insights! Stay tuned! 📚✨
#DeepLearning #Python #AI #MachineLearning #TensorFlow #NeuralNetworks
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Written by
Rashid Ul Haq
Rashid Ul Haq
I am a passionate AI and machine learning expert with extensive experience in deep learning, TensorFlow, and advanced data analytics. Having completed numerous specializations and projects, I have a wealth of knowledge and practical insights into the field. I am sharing my journey and expertise through detailed articles on neural networks, deep learning frameworks, and the latest advancements in AI technology.