Data Structures, Algorithms, and Machine Learning Optimization

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6:27:38

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1.1 Orientation to the Machine Learning Foundations Series-1 j225u2zw.mp4

03:06

1.2 A Brief History of Data-1 2b15gjsa.mp4

06:51

1.3 A Brief History of Algorithms-1 t7p43t0a.mp4

05:04

1.4 Applications to Machine Learning-1 0mwik9nq.mp4

03:18

2.1 Introduction-1 3m8jqstj.mp4

08:14

2.2 Constant Time-1 mvmme7tc.mp4

09:26

2.3 Linear Time-1 lbtul2fd.mp4

07:57

2.4 Polynomial Time-1 azvfdac9.mp4

05:26

2.5 Common Runtimes-1 oc6ntre1.mp4

06:47

2.6 Best versus Worst Case-1 3s3zu6v7.mp4

09:23

3.1 Lists-1 xq8t62k3.mp4

03:28

3.2 Arrays-1 ng1ex7u7.mp4

08:55

3.3 Linked Lists-1 9mdvvbl1.mp4

03:03

3.4 Doubly-Linked Lists-1 mg3e611v.mp4

01:24

3.5 Stacks-1 km4ks6ba.mp4

04:16

3.6 Queues-1 fb1syknh.mp4

01:48

3.7 Deques-1 qiae1dix.mp4

05:19

4.1 Binary Search-1 sgp6ncjp.mp4

17:40

4.2 Bubble Sort-1 0rciok4o.mp4

09:20

4.3 Merge Sort-1 bvp4hwmr.mp4

16:14

4.4 Quick Sort-1 alqbo35e.mp4

15:05

5.1 Maps and Dictionaries-1 jlghq9fx.mp4

04:32

5.2 Sets-1 1fu7r32o.mp4

03:57

5.3 Hash Functions-1 69rzqd81.mp4

06:50

5.4 Collisions-1 hueopo2v.mp4

05:13

5.5 Load Factor-1 1o6rzf14.mp4

02:17

5.6 Hash Maps-1 xgfoxj80.mp4

02:50

5.7 String Keys-1 9yrxbkmz.mp4

03:17

5.8 Hashing in ML-1 hd4ofvqr.mp4

02:13

6.1 Introduction-1 vige31cl.mp4

03:37

6.2 Decision Trees-1 a1kf47jd.mp4

15:58

6.3 Random Forests-1 tdzchlbt.mp4

08:05

6.4 XGBoost - Gradient-Boosted Trees-1 nqqqkuut.mp4

09:57

6.5 Additional Concepts-1 pe96vxli.mp4

02:49

7.1 Introduction-1 zrs05h1z.mp4

02:58

7.2 Directed versus Undirected Graphs-1 8owxblcg.mp4

01:43

7.3 DAGs - Directed Acyclic Graphs-1 6e7vwpti.mp4

10:33

7.4 Additional Concepts-1 gs4yyv04.mp4

01:26

7.5 Bonus - Pandas DataFrames-1 0qdxrtbz.mp4

01:50

7.6 Resources for Further Study of DSA-1 yotxi7qk.mp4

01:41

8.1 Statistics versus Machine Learning-1 ryhdp03c.mp4

12:28

8.2 Objective Functions-1 mfqn5miw.mp4

07:56

8.3 Mean Absolute Error-1 o25rl6gv.mp4

02:08

8.4 Mean Squared Error-1 2ijm8zs5.mp4

08:01

8.5 Minimizing Cost with Gradient Descent-1 6csagyw7.mp4

06:59

8.6 Gradient Descent from Scratch with PyTorch-1 b9c5to06.mp4

25:58

8.7 Critical Points-1 dqtjm5pa.mp4

08:46

8.8 Stochastic Gradient Descent-1 4ugi2e0l.mp4

16:44

8.9 Learning Rate Scheduling-1 rbn74jj8.mp4

12:47

8.10 Maximizing Reward with Gradient Ascent-1 sjmahkxl.mp4

02:55

9.1 Jacobian Matrices-1 locjc786.mp4

07:06

9.2 Second-Order Optimization and Hessians-1 p3snmxzz.mp4

05:26

9.3 Momentum-1 2t8nos0w.mp4

02:38

9.4 Adaptive Optimizers-1 iiaajhex.mp4

07:19

9.5 Congratulations and Next Steps-1 2f5dvgbj.mp4

08:26

Data Structures, Algorithms, and Machine Learning Optimization - Introduction-1 obd37x4i.mp4

02:55

Data Structures, Algorithms, and Machine Learning Optimization - Summary-1 c7y8c31a.mp4

00:52

Topics-1 6t08jff9.mp4

00:27

Topics-1 9l2kqjwl.mp4

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Topics-1 14lwnrox.mp4

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Topics-1 h8oxrxkq.mp4

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Topics-1 il0kt54q.mp4

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Topics-1 k8xqbx14.mp4

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Topics-1 lnxw7w1d.mp4

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Topics-1 ojmlg5t3.mp4

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Topics-1 z0h0ef88.mp4

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