Best Resources for Learning Deep Learning with Python

 📘 Best Books

1. Deep Learning with Python by François Chollet


Author of Keras


Teaches deep learning from scratch using Keras and TensorFlow


Beginner-friendly, intuitive explanations


📚 Link to book


2. Neural Networks and Deep Learning by Michael Nielsen (Free)


Great introduction to core concepts with visual explanations


Pure Python (no frameworks)


Excellent for foundational understanding


🌐 Read online for free


3. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville


More theoretical and mathematical


Recommended for advanced learners and researchers


📚 Deep Learning Book


🎓 Best Online Courses

1. Deep Learning Specialization – Andrew Ng (Coursera)


Covers neural networks, CNNs, RNNs, transformers


Uses Python and TensorFlow/Keras


Perfect structured path for beginners


🎓 Deep Learning Specialization


2. Fast.ai – Practical Deep Learning for Coders


Hands-on, top-down approach


Great for developers with Python experience


Focus on PyTorch


🌐 Fast.ai Course


3. CS231n – Stanford University


Deep Learning for Computer Vision


In-depth explanations of CNNs, backprop, optimization


Python + NumPy + PyTorch/TensorFlow


📚 CS231n Lecture Notes


🛠️ Best Python Libraries for Deep Learning

Library Description

TensorFlow Popular end-to-end open-source library from Google

Keras High-level API (now built into TensorFlow)

PyTorch Flexible and intuitive; widely used in research

Fastai Built on PyTorch; simplifies training

Hugging Face Transformers Pretrained models for NLP and vision

🎥 YouTube Channels Worth Following


DeepLizard – Intuitive deep learning tutorials


Sentdex – Full deep learning with Python series (Keras, TensorFlow, PyTorch)


CodeEmporium – Visual breakdowns of key concepts


Two Minute Papers – Research papers explained simply


🧪 Hands-On Practice Platforms


Kaggle: Competitions, kernels, and datasets to practice deep learning

🌐 Kaggle


Google Colab: Free cloud GPUs to run your deep learning notebooks

🌐 Colab


Papers With Code: Browse SOTA models with code

🌐 Papers With Code


📊 Projects You Can Build


Digit classifier using MNIST


Image caption generator


Sentiment analysis model


Face recognition system


Style transfer using CNNs


Music or text generator using RNNs or transformers


🧠 Tips for Learning Deep Learning


Start small: Learn the basics of linear algebra, calculus, and probability.


Use Jupyter or Colab notebooks for experiments.


Don’t just watch videos — code everything yourself.


Rebuild classic models like LeNet, AlexNet, and ResNet from scratch.


Practice with real datasets — not just toy problems.

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Transfer Learning with Pre-trained Models: A Practical Guide

Understanding the Vanishing Gradient Problem in Neural Networks

Building a Neural Network with PyTorch: A Beginner’s Guide

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