TGx:Data Structures, Algorithms, and Machine Learning Optimization--- Coursesghar

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Data Structures, Algorithms, and Machine Learning Optimization--- Coursesghar

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English

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14.52 GB

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59eb289c7978327b733380bc590bcef1cd6e0dc7

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16-06-2021 20:11

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File	Size
!! IMPORTANT Note !!.txt	283.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
1.1 Orientation to the Machine Learning Foundations Series-1_j225u2zw.mp4	85.19 MB
1.2 A Brief History of Data-1_2b15gjsa.mp4	289.23 MB
1.3 A Brief History of Algorithms-1_t7p43t0a.mp4	108.26 MB
1.4 Applications to Machine Learning-1_0mwik9nq.mp4	81.28 MB
2.1 Introduction-1_3m8jqstj.mp4	321.76 MB
2.2 Constant Time-1_mvmme7tc.mp4	328.24 MB
2.3 Linear Time-1_lbtul2fd.mp4	271.95 MB
2.4 Polynomial Time-1_azvfdac9.mp4	197.94 MB
2.5 Common Runtimes-1_oc6ntre1.mp4	146.48 MB
2.6 Best versus Worst Case-1_3s3zu6v7.mp4	573.98 MB
3.1 Lists-1_xq8t62k3.mp4	76.56 MB
3.2 Arrays-1_ng1ex7u7.mp4	268.69 MB
3.3 Linked Lists-1_9mdvvbl1.mp4	96.93 MB
3.4 Doubly-Linked Lists-1_mg3e611v.mp4	44.79 MB
3.5 Stacks-1_km4ks6ba.mp4	124.66 MB
3.6 Queues-1_fb1syknh.mp4	66.32 MB
3.7 Deques-1_qiae1dix.mp4	206.00 MB
4.1 Binary Search-1_sgp6ncjp.mp4	661.72 MB
4.2 Bubble Sort-1_0rciok4o.mp4	399.75 MB
4.3 Merge Sort-1_bvp4hwmr.mp4	837.90 MB
4.4 Quick Sort-1_alqbo35e.mp4	840.97 MB
5.1 Maps and Dictionaries-1_jlghq9fx.mp4	184.82 MB
5.2 Sets-1_1fu7r32o.mp4	114.76 MB
5.3 Hash Functions-1_69rzqd81.mp4	303.57 MB
5.4 Collisions-1_hueopo2v.mp4	195.45 MB
5.5 Load Factor-1_1o6rzf14.mp4	98.78 MB
5.6 Hash Maps-1_xgfoxj80.mp4	108.40 MB
5.7 String Keys-1_9yrxbkmz.mp4	177.06 MB
5.8 Hashing in ML-1_hd4ofvqr.mp4	129.88 MB
6.1 Introduction-1_vige31cl.mp4	148.49 MB
6.2 Decision Trees-1_a1kf47jd.mp4	581.69 MB
6.3 Random Forests-1_tdzchlbt.mp4	316.69 MB
6.4 XGBoost - Gradient-Boosted Trees-1_nqqqkuut.mp4	360.52 MB
6.5 Additional Concepts-1_pe96vxli.mp4	72.80 MB
7.1 Introduction-1_zrs05h1z.mp4	71.76 MB
7.2 Directed versus Undirected Graphs-1_8owxblcg.mp4	41.89 MB
7.3 DAGs - Directed Acyclic Graphs-1_6e7vwpti.mp4	309.54 MB
7.4 Additional Concepts-1_gs4yyv04.mp4	47.01 MB
7.5 Bonus - Pandas DataFrames-1_0qdxrtbz.mp4	57.23 MB
7.6 Resources for Further Study of DSA-1_yotxi7qk.mp4	53.92 MB
8.1 Statistics versus Machine Learning-1_ryhdp03c.mp4	558.01 MB
8.10 Maximizing Reward with Gradient Ascent-1_sjmahkxl.mp4	87.10 MB
8.2 Objective Functions-1_mfqn5miw.mp4	389.68 MB
8.3 Mean Absolute Error-1_o25rl6gv.mp4	151.63 MB
8.4 Mean Squared Error-1_2ijm8zs5.mp4	384.49 MB
8.5 Minimizing Cost with Gradient Descent-1_6csagyw7.mp4	160.98 MB
8.6 Gradient Descent from Scratch with PyTorch-1_b9c5to06.mp4	942.92 MB
8.7 Critical Points-1_dqtjm5pa.mp4	230.93 MB
8.8 Stochastic Gradient Descent-1_4ugi2e0l.mp4	596.05 MB
8.9 Learning Rate Scheduling-1_rbn74jj8.mp4	480.78 MB
9.1 Jacobian Matrices-1_locjc786.mp4	218.93 MB
9.2 Second-Order Optimization and Hessians-1_p3snmxzz.mp4	112.03 MB
9.3 Momentum-1_2t8nos0w.mp4	113.83 MB
9.4 Adaptive Optimizers-1_iiaajhex.mp4	257.70 MB
9.5 Congratulations and Next Steps-1_2f5dvgbj.mp4	224.38 MB
Data Structures, Algorithms, and Machine Learning Optimization - Introduction-1_obd37x4i.mp4	209.38 MB
Data Structures, Algorithms, and Machine Learning Optimization - Summary-1_c7y8c31a.mp4	60.61 MB
Join Our Telegram Group For More Updates !!!.url	114.00 B
Topics-1_14lwnrox.mp4	38.42 MB
Topics-1_6t08jff9.mp4	28.34 MB
Topics-1_9l2kqjwl.mp4	30.25 MB
Topics-1_h8oxrxkq.mp4	33.11 MB
Topics-1_il0kt54q.mp4	22.91 MB
Topics-1_k8xqbx14.mp4	25.43 MB
Topics-1_lnxw7w1d.mp4	39.77 MB
Topics-1_ojmlg5t3.mp4	47.83 MB
Topics-1_z0h0ef88.mp4	19.26 MB
Uploaded by [Coursesghar.com].txt	1.10 kB
Visit coursesghar.com for more awesome tutorials.url	114.00 B
[TGx]Downloaded from torrentgalaxy.to .txt	564.00 B

Description

Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing.

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010.

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