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Beginner Challenges Completed
Beginner Python Challenges
Challenge 10: Check If a String Is a Palindrome
Challenge 11: Merge Two Dictionaries (Safely)
Challenge 12: Count Items in a List
Challenge 1: Reverse a String Safely
Challenge 2: Count Words in a Sentence
Challenge 3: Find the Largest Number in a List
Challenge 4: Count Character Frequency
Challenge 5: Filter Even Numbers
Challenge 6: Even or Odd (With Input Validation)
Challenge 7: Sum Numbers in a List
Challenge 8: Remove Duplicates from a List
Challenge 9: Find the Second Largest Number in a List
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Neural Net Template
Building a Neural Network Template in Python — Gradients and the Backward Pass
Building a Neural Network Template in Python — Loss Functions and Error Intuition
Building a Neural Network Template in Python — Project Setup and Core Abstractions
Building a Neural Network Template in Python — The Training Loop and Stochastic Gradient Descent
Building a Neural Network Template in Python — Your First Complete Model
Neural Networks From Scratch in Python
Activation Functions in Neural Networks — Why a Network Without Them Cannot Learn
Backpropagation Step by Step — Computing Gradients for a Single Neuron
Backpropagation Through a Layer — How Neural Networks Learn at Scale
Backpropagation With Activation Functions — Completing the Gradient Chain
Batch and Mini-Batch Training — Removing the Last Loop
Building a Neural Network Layer in Python — From Single Neurons to Real Structure
Forward Propagation in Neural Networks — How Data Flows Through the Network
Gradients in Neural Networks — Why Learning Requires Derivatives
L2 Regularization (Weight Decay) — Teaching Neural Networks to Generalize
Loss Functions in Neural Networks — Measuring How Wrong the Network Is
Neural Networks From Scratch in Python — What a Neuron Really Computes
Reading Loss Curves and Detecting Overfitting — When Learning Goes Wrong
Training a Neural Network End to End — The Complete Learning Loop in Python
Vectorized Backpropagation With NumPy — Training at Real Scale
Vectorizing a Neural Network With NumPy — From Loops to Real Speed
Neural Networks From Scratch in Python (Part I)
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Python Coding Challenges
Sparse Neural Networks From Scratch in Python
Activation Sparsity in Python: Why Most Neurons Should Stay Silent
Dense vs Sparse: A Fair Benchmark in Python
Dynamic Sparse Training in Python: Let the Network Rewire Itself
Magnitude‑Based Pruning in Python: Removing Weights That Don’t Matter
Training Sparse Neural Networks From Scratch (No Dense Pretraining)
What Are Sparse Neural Networks? (A Python‑First Introduction)
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Contract Renewal
August 17, 2026
Contract Renewal Software for Private Equity Portfolio Companies: Managing Vendor Spend, SaaS Costs, Contract Synergies, and Renewal Savings Across Multiple Businesses
August 17, 2026
Best Contract Renewal Software for Finance Teams and CFOs: Managing Renewal Spend, Budgets, Forecasts, Commitments, and Cost Savings
August 17, 2026
Contract Renewal Supplier Scorecards: How to Combine Performance, Cost, Risk, Service Quality, and Renewal History Before You Decide to Renew
August 17, 2026
Contract Renewal Business Reviews: How to Challenge Usage, Business Value, Scope, and Supplier Need Before You Renew
August 17, 2026
Contract Renewal Termination Management: How to Send Notices on Time, Preserve Evidence, Track Acknowledgments, and Avoid Invalid Cancellations
August 17, 2026
Contract Renewal Supplier Consolidation: How to Combine Multiple Contracts, Align Renewal Dates, Increase Negotiation Leverage, and Reduce Vendor Sprawl
August 17, 2026
Contract Renewal Decision Management: How to Structure Renew, Renegotiate, Reduce, Replace, Extend, or Terminate Decisions
August 17, 2026
Contract Renewal Spend Optimization: How to Find Unused Licenses, Duplicate Services, Scope Creep, Price Increases, and Cost-Reduction Opportunities Before Renewal
August 16, 2026
Contract Renewal Budget Planning: How Finance Teams Can Forecast Recurring Spend, Supplier Increases, Savings, and Future Commitments
August 16, 2026
Contract Renewal Cost Allocation: How to Split Shared Supplier Costs Across Departments, Business Units, Cost Centers, and Legal Entities
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