Wednesday, May 25, 2022
Technical Topic Tuesday -59- Machine Learning #machinelearning #datascience #technology #ai #ml
@Passion, People & Purpose #machinelearning #datascience #technology #ai #ml Machine Learning Purpose: to help enhance not only many industrial, enterprise & professional processes but also our everyday living What? a subset of artificial intelligence focused on building systems that can learn from historical data, identify patterns, & make logical decisions with little to no human intervention How? 1. Collecting and preparing the Data 2. Choosing a Model 3. Training the Model 4. Evaluating the Model 5. Parameter Tuning 6. Making Predictions Let me know when is comes to build a ML model what do you see the major challenge? or any other steps would you like add to achieve more accuracy? Happy Learning and Sharing! Until we meet, happy leading, and let's lead together. Stay safe. Bye for now. Source - https://www.google.com/ #SoLeadSaturday Podcast/Show https://www.netapp.com/artificial-intelligence/what-is-machine-learning/ https://www.sas.com/en_us/insights/analytics/machine-learning.html https://quantilus.com/why-is-machine-learning-important-and-how-will-it-impact-business/ https://www.simplilearn.com/tutorials/machine-learning-tutorial/machine-learning-steps https://developers.google.com/machine-learning/guides/rules-of-ml https://neptune.ai/blog/improving-machine-learning-deep-learning-models Find me on - YoutTube - https://bit.ly/3dA0Qko #SoLeadSaturday Community Website - https://vaishalilambe.club/ Twitter - https://bit.ly/3Id2AMx LinkedIn - https://bit.ly/3tBe4Ft Instagram - @PassionPeoplePurpose Tiktok - @PassionPeoplePurpose Website - https://bit.ly/3GETAiS Facebook - https://bit.ly/3ry025c Apple Podcasts - https://apple.co/3KikjEi Google Podcasts - https://bit.ly/3fwzX0q Anchor - https://bit.ly/3rrij3R Spotify - https://spoti.fi/3Fw1tG4 Breaker - https://bit.ly/3Ib6ZQf Overcast - https://bit.ly/33GATNe Pocket casts - https://bit.ly/3tuNmyu Radio Public - https://bit.ly/33sfHLc Castbox - https://bit.ly/3KdUpl7 Video Editor - Vaishali Lambe Thumbnail Designer - Vaishali Lambe
Subscribe to:
Post Comments (Atom)
Most watched
-
Quick answer: Qwen 3 32B needs about 22.7 GB of VRAM at Q4_K_M with a 4k-token context — a used 24 GB card like the RTX 3090 is the minimu...
-
Using GPUs in TensorFlow, TensorBoard in notebooks, finding new datasets, & more! (#AskTensorFlow) [Collection] In a special live ep...
-
Inside TensorFlow: Summaries and TensorBoard [Collection] Take an inside look into the TensorFlow team’s own internal training sessions-...
-
Quick answer: For an 8B model at Q4, local electricity costs roughly $0.05 per million output tokens — against about $0.53 per million ble...
-
Quick answer: For machine-learning work, buy an external SSD over a spinning hard drive, pick USB 3.2 Gen 2 (10 Gbps) or faster, and size f...
-
Quick answer: DeepSeek V4 Flash is a Mixture-of-Experts model: all 284 billion parameters must be stored, so combined RAM plus VRAM — not ...
-
Quick answer: Yes, for most budget builders: a used RTX 3090 is still the cheapest route to 24 GB of VRAM with full CUDA support. Capacity...
-
Hey everyone This is Ujjwal kapil B.tech 2nd year Ai and Ml In this video we covered one of the most important question "COLLEGE KAB O...
-
Quick answer: The cheapest path to serious local-AI VRAM is not one new flagship card — it is two used ones. A single RTX 5090 costs $1,999...
-
Quick answer: FLUX.1 dev needs about 10 GB of VRAM at Q4/NF4 quantization for 1024×1024 image generation, so a 12 GB GPU such as the GeFor...
No comments:
Post a Comment