Intro to Python Deep Learning libraries- Tensorflow, Keras, PyTorch | Programming foundations for ML
Welcome to this foundational lecture on deep learning frameworks in Python – TensorFlow, Keras, and PyTorch. If you are new to machine learning or just getting started with deep learning, this video will give you a comprehensive overview of how to build and train neural networks using the most popular Python libraries.
🧠 Google Colab Notebook (Keras + PyTorch): https://colab.research.google.com/drive/1dIRirQGokkX6rM8g_3HODPDdktUoT_0K?usp=sharing
In this hands-on tutorial, you will learn:
What is a neural network and why it is called a universal function approximator
The role of activation functions like ReLU and Sigmoid
The difference between shallow and deep neural networks
When to use deep learning vs traditional machine learning
How to build a simple binary classification model using Keras with TensorFlow backend
How to build the same model from scratch using PyTorch
The pros and cons of Keras, TensorFlow, and PyTorch
Why GPU acceleration matters in deep learning
How to visualize your training progress with accuracy and loss curves
You will also see a live coding demo in Google Colab where we:
Generate a synthetic dataset using make_classification from Scikit-learn
Scale the dataset using StandardScaler
Train a simple deep neural network in Keras and then in PyTorch
Visualize model performance with Matplotlib
Compare training and test accuracy across frameworks
Whether you are trying to understand which framework is right for your project, or you simply want to get comfortable writing your first deep learning models, this video will provide a structured and intuitive learning experience.
Welcome to this foundational lecture on deep learning frameworks in Python – TensorFlow, Keras, and PyTorch. If you are new to machine learning or just getting started with deep learning, this video will give you a comprehensive overview of how to build and train neural networks using the most popular Python libraries.
🧠 Google Colab Notebook (Keras + PyTorch): https://colab.research.google.com/drive/1dIRirQGokkX6rM8g_3HODPDdktUoT_0K?usp=sharing
In this hands-on tutorial, you will learn:
What is a neural network and why it is called a universal function approximator
The role of activation functions like ReLU and Sigmoid
The difference between shallow and deep neural networks
When to use deep learning vs traditional machine learning
How to build a simple binary classification model using Keras with TensorFlow backend
How to build the same model from scratch using PyTorch
The pros and cons of Keras, TensorFlow, and PyTorch
Why GPU acceleration matters in deep learning
How to visualize your training progress with accuracy and loss curves
You will also see a live coding demo in Google Colab where we:
Generate a synthetic dataset using make_classification from Scikit-learn
Scale the dataset using StandardScaler
Train a simple deep neural network in Keras and then in PyTorch
Visualize model performance with Matplotlib
Compare training and test accuracy across frameworks
Whether you are trying to understand which framework is right for your project, or you simply want to get comfortable writing your first deep learning models, this video will provide a structured and intuitive learning experience.
