CS5720 - Week 1
Slide 18 of 20

Tools and Libraries Overview

TensorFlow
Google's Production-Ready Platform
End-to-end open-source platform for machine learning with powerful tools for research and production deployment.
Keras Integration Production Ready Mobile/Web Deploy Large Community
import tensorflow as tf model = tf.keras.Sequential([...])
PyTorch
Dynamic and Research-Friendly
Dynamic neural network framework with eager execution, perfect for research and experimentation.
Dynamic Graphs Pythonic Research Focus GPU Acceleration
import torch import torch.nn as nn
Scikit-Learn
Simple and Efficient ML Tools
Simple and efficient tools for predictive data analysis, built on NumPy, SciPy, and matplotlib.
Easy to Use Well Documented Classical ML Data Preprocessing
from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split
JAX
NumPy-Compatible ML Research
JAX is NumPy on steroids with autograd and XLA compilation for high-performance ML research.
Functional Programming JIT Compilation Automatic Differentiation High Performance
import jax.numpy as jnp import flax.linen as nn

📊 Framework Comparison

Framework Best For Learning Curve Industry Adoption Deployment
TensorFlow Production, Mobile/Web, Large Scale Moderate Very High Excellent
PyTorch Research, Experimentation, Prototyping Easy High Good
Scikit-Learn Classical ML, Beginners, Data Analysis Very Easy Universal Limited
JAX High-Performance Research, Functional Programming Hard Growing Limited
📊
Data & Visualization
  • NumPy
    Fundamental package for scientific computing
  • Pandas
    Data manipulation and analysis library
  • Matplotlib
    Comprehensive plotting and visualization
  • Seaborn
    Statistical data visualization
🔧
Development Tools
  • Jupyter Notebooks
    Interactive development environment
  • Google Colab
    Free cloud-based Jupyter environment
  • VS Code
    Powerful code editor with ML extensions
  • Git & GitHub
    Version control and collaboration
⚡
Performance & Monitoring
  • TensorBoard
    Visualization toolkit for machine learning
  • Weights & Biases
    Experiment tracking and model management
  • MLflow
    Machine learning lifecycle management
  • Optuna
    Hyperparameter optimization framework
🚀
Deployment & Production
  • Docker
    Containerization for reproducible environments
  • FastAPI
    Modern web framework for building APIs
  • Streamlit
    Create web apps for machine learning
  • Hugging Face
    Pre-trained models and deployment platform

🛠️ Quick Setup Guide

Beginner Setup
Intermediate Setup
Advanced Setup
Cloud Setup
1 Install Python 3.8+

Download from python.org or use Anaconda distribution for easier package management.

# Download Anaconda from anaconda.com # Or install Python directly and use pip python --version # Check installation
2 Install Essential Libraries

Start with the core libraries needed for neural network development.

pip install tensorflow pandas numpy matplotlib scikit-learn jupyter
3 Verify Installation

Test that everything is working correctly with a simple script.

import tensorflow as tf import numpy as np import pandas as pd import matplotlib.pyplot as plt print("TensorFlow version:", tf.__version__) print("GPU available:", tf.config.list_physical_devices('GPU'))
1 Create Virtual Environment

Isolate your project dependencies to avoid conflicts.

# Using conda conda create -n ml-env python=3.9 conda activate ml-env # Using venv python -m venv ml-env source ml-env/bin/activate # Linux/Mac # ml-env\Scripts\activate # Windows
2 Install Full ML Stack

Include additional tools for visualization and experimentation.

pip install tensorflow pytorch torchvision scikit-learn pip install pandas numpy matplotlib seaborn plotly pip install jupyter jupyterlab ipywidgets pip install tensorboard wandb optuna
3 Setup Development Environment

Configure your IDE and tools for optimal productivity.

# Install VS Code Python extension # Configure Jupyter kernels jupyter kernelspec list # Enable useful Jupyter extensions pip install jupyter_contrib_nbextensions jupyter contrib nbextension install --user
1 GPU Setup (NVIDIA)

Configure GPU acceleration for faster training.

# Install CUDA Toolkit 11.8 # Download from developer.nvidia.com/cuda-toolkit # Install cuDNN # Download from developer.nvidia.com/cudnn # Verify GPU support nvidia-smi python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
2 Performance Libraries

Install optimized libraries for production workloads.

# Intel optimizations pip install intel-tensorflow # RAPIDS for GPU-accelerated data science conda install -c rapidsai -c nvidia -c conda-forge rapids=23.10 # JAX for high-performance research pip install jax[cuda] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
3 Production Tools

Tools for model deployment and monitoring.

pip install docker fastapi uvicorn streamlit pip install mlflow dvc hydra-core pip install transformers datasets pip install onnx onnxruntime
1 Google Colab (Free)

Start immediately with no installation required.

# Go to colab.research.google.com # Click "New Notebook" # Enable GPU: Runtime > Change runtime type > GPU # Check GPU availability !nvidia-smi import tensorflow as tf print("GPU available:", tf.config.list_physical_devices('GPU'))
2 Cloud Platforms

Professional cloud environments for larger projects.

# AWS SageMaker # - Jupyter notebooks with pre-configured ML environments # - Managed training and deployment # Google Cloud AI Platform # - Vertex AI for end-to-end ML workflows # - Pre-configured deep learning VMs # Azure Machine Learning # - Managed notebooks and compute instances
3 Benefits of Cloud

Advantages of cloud-based development.

✓ No local setup required ✓ Access to powerful GPUs/TPUs ✓ Automatic scaling ✓ Collaboration features ✓ Built-in version control ✓ Easy deployment options
← Previous Next →
Prepared by Dr. Gorkem Kar