▲
0
0

report

Udemy - Loss Functions and Learning Rates for Data Scientists

seeders: 1
leechers: 11
Download torrent
Added 5 hours ago by freecoursewb in Other
Download Fast Safe Anonymous
movies, software, shows...

Files

Udemy - Loss Functions and Learning Rates for Data Scientists (Size: 967 MB)
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ~Get Your Files Here !
  1 - Loss Functions How Neural Networks Measure Mistakes
  1 - Introduction — The Engine of Learning.mp4 15.5 MB
  1 - Practice Quiz – 1.html 20.3 KB
  10 - From MSE Numbers to Weight Updates.html 6.7 KB
  11 - Common Regression Losses.mp4 10.9 MB
  12 - When Loss is High vs Low.mp4 6.5 MB
  13 - Choosing a Regression Loss MSE, MAE, Huber.html 6.7 KB
  14 - Loss Curves — The Shape Of Learning.mp4 10.5 MB
  15 - Final Summary.mp4 23.2 MB
  16 - Loss Curves Watching Learning Happen.html 6.7 KB
  2 - Learning Rate Fundamentals – How Neural Networks Take Steps Toward Accuracy
  0 - The Sweet Spot — Balanced Learning.mp4 6.9 MB
  10 - Practice Quiz – 10.html 20.1 KB
  17 - Introduction — The Speed of Learning.mp4 14 MB
  18 - Story Analogy — Walking Down a Mountain.mp4 12.3 MB
  19 - What is the Learning Rate.mp4 9.7 MB
  20 - Learning Rate How Big Is Each Step.html 6.2 KB
  21 - Why Small Learning Rate Can Fail.mp4 5.5 MB
  22 - Why Large Learning Rate Can Fail.mp4 6.5 MB
  23 - Too Small, Too Large What Can Go Wrong.html 6.5 KB
  24 - Python Demo — Too Small, Too Large, Just Right.mp4 7.5 MB
  25 - Loss Curves Reveal Everything.mp4 8.5 MB
  26 - Seeing Learning Rate Through Experiments.html 6.5 KB
  27 - How Learning Rate Impacts Generalization.mp4 9.4 MB
  28 - Learning Rate Schedules — Making Training Smart.mp4 10.5 MB
  29 - Learning Rate, Generalization, and Schedules.html 6.6 KB
  3 - Mean Squared Error (MSE) in Neural Networks – The Core Loss for Regression
  11 - Practice Quiz –11.html 20.3 KB
  12 - Practice Quiz – 12.html 20.3 KB
  13 - Practice Quiz – 13.html 20.3 KB
  14 - Practice Quiz – 14.html 20.6 KB
  15 - Practice Quiz – 15.html 20.4 KB
  34 - Introduction — The Most Popular Loss Function.mp4 10 MB
  35 - When Do We Use MSE — The Real-Life Use Cases.mp4 7.1 MB
  36 - Where MSE Shows Up in Real Models.html 6.4 KB
  37 - What Is MSE — Intuitive Explanation.mp4 6.2 MB
  38 - Mini Example — Simple Linear Relationship.mp4 7.8 MB
  39 - MSE Formula, Step by Step.html 6.3 KB
  4 - Binary Cross Entropy – The Loss Function for Yes No Decisions
  16 - Practice Quiz – 16.html 20 KB
  17 - Practice Quiz – 17.html 19.7 KB
  18 - Practice Quiz – 18.html 19.9 KB
  19 - Practice Quiz – 19.html 20.2 KB
  20 - Practice Quiz – 20.html 20.2 KB
  49 - Introduction — The Loss Function for Classification.mp4 10.7 MB
  5 - Categorical Cross Entropy – How Neural Networks Learn Multi‑Class Decisions
  21 - Practice Quiz – 21.html 20.4 KB
  22 - Practice Quiz – 22.html 20.2 KB
  23 - Practice Quiz – 23.html 20.4 KB
  24 - Practice Quiz – 24.html 20.2 KB
  25 - Practice Quiz – 25.html 20.2 KB
  6 - The Vanishing Gradient Problem – Why Deep Networks Forget to Learn
  26 - Practice Quiz – 26.html 20.2 KB
  27 - Practice Quiz – 27.html 20.1 KB
  28 - Practice Quiz – 28.html 20.4 KB
  29 - Practice Quiz – 29.html 20.1 KB
  30 - Practice Quiz – 30.html 20.7 KB
  7 - Adaptive Learning Rate Algorithms – RMSprop, Adam & Modern Optimization
  100 - Introduction — Adaptive Learning Rates.mp4 13.6 MB
  101 - Why Fixed Learning Rates Are A Problem.mp4 11.7 MB
  102 - Why One Fixed Learning Rate Isn’t Enough.html 6.4 KB
  103 - RMSprop — Root Mean Square Propagation.mp4 11.4 MB
  104 - Why RMSprop Works.mp4 8.4 MB
  105 - RMSprop Remembering Squared Gradients.html 6.3 KB
  106 - Adam — Adaptive Moment Estimation.mp4 13 MB
  107 - Why Adam Dominates Deep Learning.mp4 5.6 MB
  108 - Adam Momentum Meets RMSprop.html 6.3 KB
  109 - Visualizing Adam vs SGD vs RMSprop.mp4 12.2 MB
  110 - When Adam Might Not Be Ideal.mp4 7.2 MB
  111 - How SGD, RMSprop, and Adam Move Differently.html 6.4 KB
  112 - Python Mini-Demo — Using RMSprop & Adam.mp4 7.2 MB
  113 - Summary Comparison Table.mp4 23.7 MB
  114 - Final Summary.mp4 25 MB
  115 - Practical Choices When to Use Which Optimizer.html 6.5 KB
  31 - Practice Quiz – 31.html 20.4 KB
  32 - Practice Quiz – 32.html 20 KB
  33 - Practice Quiz – 33.html 20 KB
  34 - Practice Quiz – 34.html 20 KB
  35 - Practice Quiz – 35.html 20 KB
  8 - Batch Size Impact on Convergence – How Mini‑Batches Shape Training
  116 - Introduction — Batch Size And Training Dynamics.mp4 9.8 MB
  117 - What Is Batch Size (Concept).mp4 13 MB
  118 - What Batch Size Really Means.html 6.2 KB
  119 - Gradient Behavior With Different Batch Sizes.mp4 7.9 MB
  120 - Full Batch Gradient Descent.mp4 8.1 MB
  121 - Stochastic Gradient Descent (SGD).mp4 8.3 MB
  122 - Noise vs Stability How Batch Size Shapes Gradients.html 6.3 KB
  123 - Why Mini-Batch Is The Best Compromise.mp4 8.4 MB
  124 - How Batch Size Affects Convergence Speed.mp4 8.6 MB
  125 - Why Mini‑Batch Is the Sweet Spot.html 6.3 KB
  126 - Generalization Vs Optimization.mp4 7.1 MB
  127 - How Batch Size Influences Sharp Vs Flat Minima.mp4 9.9 MB
  128 - Sharp vs Flat Minima Why Batch Size Affects Generalization.html 6.5 KB
  129 - Practical Guidelines For Choosing Batch Size.mp4 7.6 MB
  130 - Python Mini-Demo — Different Batch Sizes.mp4 8.7 MB
  131 - Final Summary.mp4 23 MB
  132 - Practical Batch Size Choices.html 6.5 KB
  36 - Practice Quiz – 36.html 19.8 KB
  37 - Practice Quiz – 37.html 19.9 KB
  38 - Practice Quiz – 38.html 19.8 KB
  39 - Practice Quiz – 39.html 20 KB
  40 - Practice Quiz – 40.html 20.1 KB
  83 - Introduction — The Vanishing Gradient Problem.mp4 11.9 MB
  84 - What Are Gradients.mp4 7.1 MB
  85 - Gradients as Learning Signals.html 6.2 KB
  86 - Why Do Gradients Vanish (The Math Intuition).mp4 8.4 MB
  87 - Visualizing The Effect.mp4 8.9 MB
  88 - Why Gradients Disappear in Deep Networks.html 6.4 KB
  89 - Vanishing Gradients in RNNs.mp4 8.6 MB
  90 - Why RNNs Feel It Even More.html 6.2 KB
  91 - ReLU Activation.mp4 8.9 MB
  92 - He & Xavier Initialization.mp4 6.8 MB
  93 - Batch Normalization.mp4 4.4 MB
  94 - Resnets (Skip Connections).mp4 7.6 MB
  95 - LSTM & GRU For Sequences.mp4 7.7 MB
  96 - The Toolbox for Fighting Vanishing Gradients.html 7 KB
  97 - Mini Python Demo — Seeing Gradients Vanish.mp4 6.4 MB
  98 - Summary.mp4 15.7 MB
  99 - Seeing Vanishing Gradients and Fixing Them.html 6.6 KB
  66 - Introduction — The Loss Function For Multi-Class Learning.mp4 10.9 MB
  67 - What Is Categorical Cross Entropy — Simple Story.mp4 7.8 MB
  68 - From Yes No to Many Classes.html 6.5 KB
  69 - The Mathematical Formula (Explained Friendly).mp4 14.3 MB
  70 - Tiny Python Demo.mp4 9.3 MB
  71 - Understanding the CCE Formula.html 6.3 KB
  72 - Sentiment Classification (Numeric Scores).mp4 7 MB
  73 - Training Loop — Explained Step By Step.mp4 11.7 MB
  74 - Softmax + CCE A Natural Pair.html 6.2 KB
  75 - Testing — Sentiment Prediction.mp4 6.3 MB
  76 - Extended Use Case — Real Text Sentiment Classifier.mp4 6.3 MB
  77 - A Tiny 3‑Class Sentiment Pipeline.html 6.3 KB
  78 - Why Categorical Cross Entropy Is The Best For Multi-Class.mp4 5.4 MB
  79 - Math Concepts Required.mp4 5.8 MB
  80 - Diagram — Cross Entropy Loss Shape.mp4 5.1 MB
  81 - Final Summary.mp4 21 MB
  82 - Why CCE Is the Multi‑Class Standard.html 6.3 KB
  50 - What Is Binary Cross Entropy — Simple Story.mp4 10.3 MB
  51 - Why Regression Losses Fail for Yes No Questions.html 6.5 KB
  52 - The Mathematical Formula (But Explained Like a Friend).mp4 14.2 MB
  53 - Intuitive Table.mp4 8.5 MB
  54 - Reading the BCE Formula Without Fear.html 6.3 KB
  55 - Why BCE Is Loved In Deep Learning.mp4 6.3 MB
  56 - Where We Use BCE.mp4 6.1 MB
  57 - Mini Story — Why Cross Entropy Makes Sense.mp4 5.3 MB
  58 - Confidence Matters Why BCE Is Loved.html 6.4 KB
  59 - Python Demo.mp4 8.2 MB
  60 - The Math Concepts Needed Before Learning BCE.mp4 15.6 MB
  61 - BCE in Code What We Actually Compute.html 6.3 KB
  62 - Diagram — Cross Entropy Landscape.mp4 8 MB
  63 - Why BCE Beats MSE For Classification.mp4 7 MB
  64 - Final Summary.mp4 17.2 MB
  65 - Why BCE Beats MSE for Binary Classification.html 6.4 KB
  40 - Python Demo 1 — Calculating MSE Manually.mp4 9.6 MB
  41 - MSE + Gradient Descent — How Learning Happens.mp4 10.2 MB
  42 - From Manual MSE to Gradients.html 6.4 KB
  43 - Python Demo 2 — Training A Neural Network With MSE.mp4 15 MB
  44 - Flowchart — MSE-Based Training.mp4 8.9 MB
  45 - An End‑to‑End MSE Training Loop.html 6.5 KB
  46 - What You Must Know Before MSE.mp4 6.3 MB
  47 - Final Summary — Why MSE Matters.mp4 24.8 MB
  48 - What We Really Need to Know About MSE.html 6.4 KB
  30 - Learning Rate Finder — A Practical Technique.mp4 8.5 MB
  31 - Practical Guidelines for Choosing LR.mp4 8.7 MB
  32 - Summary — The Heart Of Training Speed.mp4 30 MB
  33 - LR Finder and Practical Rules.html 6.5 KB
  6 - Practice Quiz – 6.html 20 KB
  7 - Practice Quiz – 7.html 20.2 KB
  8 - Practice Quiz – 8.html 20.6 KB
  9 - Practice Quiz – 9.html 20.3 KB
  2 - Practice Quiz – 2.html 20.4 KB
  2 - Story Analogy — The Archer and the Target.mp4 16.2 MB
  3 - Practice Quiz – 3.html 20.3 KB
  3 - What Exactly is a Loss Function.mp4 9.6 MB
  4 - Loss as Feedback The Blindfolded Archer.html 6.9 KB
  4 - Practice Quiz – 4.html 19.9 KB
  5 - Practice Quiz – 5.html 20.2 KB
  5 - Why We Square The Error — Three Reasons.mp4 12.4 MB
  6 - Example — Predicting Salary.mp4 10.2 MB
  7 - Why We Square the Error.html 6.9 KB
  8 - Python Demo — MSE in Action.mp4 4.5 MB
  9 - Loss + Gradient Descent — The Partnership.mp4 12.2 MB

Description


Loss Functions & Learning Rates for Data Scientists
https://WebToolTip.com
Published 7/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 2h 41m | Size: 967.08 MB
Loss Functions for Data Scientists: Minimize Error & Optimize Learning Rates in ML Models
What you'll learn

Select appropriate loss functions (MSE, MAE, Huber, log loss, cross‑entropy) for regression and classification tasks.

Explain how loss functions define model “error” and guide gradient descent and parameter updates in ML models.

Interpret loss curves to detect underfitting, overfitting, bad learning rates, and instability during model training.

Tune learning rates and related hyperparameters to avoid overshooting minima, divergence, or excessively slow training.

Diagnose training issues using loss behavior and apply fixes such as changing loss, learning rate, or regularization.
Requirements

Basic Python skills, including functions, loops, and basic NumPy and/or pandas.

Working knowledge of core ML models (linear/logistic regression, basic neural networks) and the train/validation/test workflow.

Comfort with basic calculus and probability ideas (derivatives as slope, mean, variance, simple logs), though formal proofs are not required.

Ability to run code in Jupyter or a similar environment (locally or in the cloud) to follow the hands‑on examples.

Related Torrents

torrent name size uploader age seed leech
Udemy - 15-Day Weight Loss Challenge with Home-Style Meals Posted by freecoursewb in Other
1.2 GB freecoursewb 2 days 33 9
Udemy - Understanding Dementia - Beyond Memory Loss Posted by freecoursewb in Other
991.4 MB freecoursewb 1 month 13 1
Udemy - Metabolism Boost and Weight Loss Masterclass Posted by freecoursewb in Other
2 GB freecoursewb 1 month 8 1
Udemy - Microsoft Purview Data Loss Prevention Posted by freecoursewb in Other
1.9 GB freecoursewb 4 months 9 7
Udemy - Recovering from Job Loss Resilience, Confidence and Growth Posted by freecoursewb in Other
642.3 MB freecoursewb 10 months 0 0